# Presentation: 627135

## Use of 4 km, 1 hr, Precipitation Forecasts to Drive a Statistical-Distributed Hydrologic Model for Flash Flood Prediction 

- J8.4 20th Conference on Hydrology, AMS Annual Meeting 2006
- Seann Reed1, Richard Fulton1, Ziya Zhang1,2, Shucai Guan1,3
- Presented by John Schaake4
- 1Hydrology Laboratory, Office of Hydrologic Development
- National Weather Service, NOAA, Silver Spring, Maryland
- 2University Corporation for Atmospheric Research
- 3RS Information Systems, Inc.
- 4Consultant to Office of Hydrologic Development, Annapolis, MD

## A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations 

- Forecasted
- frequencies

**A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations**

- Historical

- Real-time

- Simulated peaks distribution (Qsp) (unique for each cell)

- Statistical
- Post-processor

- Distributed hydrologic model (HL-RDHM)

- Distributed hydrologic model (HL-RDHM)

- Max forecasted
- peaks

-   The statistical-distributed model outputs gridded flood frequency forecasts.
-   Here we express flood frequencies in terms of the return period (T) associated with the annual maximum flood.  T = 1/p where p is the probability of occurrence in a given year (e.g. If p = 0.5, T = 2 years).

## 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.
- A companion study has shown that the statistical-distributed approach can improve (on average) upon current, lumped model based procedures at least down to 40 km2 basin sizes.

## Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM)

- This implementation of RDHM uses:
-   2 km grid cell resolution
-   Hourly 4 km QPE and QPF grids are resampled to 2 km (nearest neighbor resampling)
-   Gridded SAC-SMA
-   Hillslope routing within each model cell
-   Cell-to-cell channel routing
**  ****Uncalibrated, a-priori parameters for Sacramento (SAC-SMA) and channel routing models **(Koren et al., 2004)
**  **RDHM showed good performance in the Distributed Model Inter-comparison Project (DMIP) (Reed et al, 2004)
-   An operational prototype version of RDHM is running at two NWS River Forecast Centers

**Notes:**

Explicit soil rainfall/runoff modeling on each computational element. 

Guan, S., F. Ding, R. Fulton, and D. Kitzmiller, 2005:  Preliminary results for the 0-1 hour multisensor precipitation nowcaster.   32nd Conference Radar Meteorology, October 24-29, Albuquerque, NM, 6R.4. 

Koren, V.I., Reed, S.M., Smith, M., Zhang, Z., Seo, D.J., 2004:  Hydrology laboratory research modeling system (HL-RMS) of the US national weather service. Journal of Hydrology, 291, 297-318.

## Multisensor Precipitation Nowcaster (MPN)

- Radar-based extrapolative nowcaster with the capability to:
-   Produce one-hour rainfall forecasts on a 4 km grid with a 5 minute update frequency (1 hour updates used here)
-   Use mosaicked WSR-88D radar data (used single radar here)
-   Use real time rain gauge data for radar bias adjustment (not used here)
-   Optionally use:
-   Storm growth/decay accounting (not used)
-   Progressive smoothing with forecast lead time (used)
-   Local (20 km) or area-averaged storm motion vectors (used local)
-   MPN is currently running at HL in real-time for a 5 radar test region in the mid-Atlantic states

## Study Basins

- OK

- AR

- INX Radar

- SRX Radar

- Interior,
- Flash flood
- basins

- Basins are well covered by either the INX or SRX radar

**Notes:**
Estimated as the average difference between the time of maximum rainfall and maximum discharge for several events.

## Forecast Experiments

**Zero QPF** :  Assumes zero future rainfall
**Persistence** (Eulerian):  Assumes the last hour of rain is repeated in future 1 hr period with no advection, followed by zero rain beyond 1 hour
**QPF:**  Uses 1 hour QPF grids from MPN followed by zero rainfall beyond 1 hour
**We have run experiments for 9 events.  Example results are presented on the next several slides.**

**Notes:**
Note:  Multisensor (radar and gauge) rainfall estimates (MPE) are used to maintain model states up until the forecast time.  MPE data are used for persistence forecasts.

Note that the comparison between persistence and MPN is a little unfair in the sense that the persistence is based on a gauge adjusted field – information that MPN doesn’t have. 

## Observed

**Example MPN 1 hr Forecast Grids Compared to Multisensor QPE**
- (similar patterns observed with some smoothing)

- Forecast for same hour

**Notes:**

Figure 5.  Left panels: observed precipitation for two hours during the 7/2004 storm.  Right panels:  forecasts issued for the same two hours at 5 UTC (top) and 6 UTC (bottom). 
Figure 5.  Left panels: observed precipitation for two hours during the 7/2004 storm.  Right panels:  forecasts issued for the same two hours at 5 UTC (top) and 6 UTC (bottom). 

## Example Forecasted Frequencies Available at 4 Times on 1/4/1998 

- 14 UTC

- 15 UTC

- 16 UTC

- 17 UTC

- Example Forecasted Frequencies Available at 4 Times on 1/4/1998

- In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.

**Notes:**
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.

## Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z

- Eldon (795 km2)

- Dutch (105 km2)

- Implicit
- statistical adjustment

- ~11 hr lead time

- ~1 hr lead time

**Notes:**
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.

## Method to Calculate “Adjusted” Peaks

**At Validation Points**

-   Probability matching was used to compute adjusted flows at validation points.
-   For implementation we can only assume a similar implicit correction if we are considering frequency-based flood thresholds at ungauged locations.

- 157 cms
- (simulated)

- 247 cms
- (adjusted)

## Lead Time = 2 hrs 

-   All results shown are for CAVESP (90 km2) and single Event (7/2004)

- Lead Time = 4 hrs

- Peak errors of different forecasts relative to simulated flows as a function of lead time

- Lead Time = 3 hrs

- Lead Time = 2 hrs

**Forecast Accuracy at Different Lead Times**

**Notes:**
Note that I did not compute the statistically adjusted peaks for Cave Springs like in the last slide because there was not enough observed data. The basic fit for this event is excellent.

## Example Results for One Basin and Several Events

- 1/4/1998

- 7/3/2004

- 6/21/2000

- 6/17/2000

**Example Results for One Basin and Several Events**

-  Lead time is a function of both basin response and QPF
-  QPF outperforms
- 0 QPF in these cases.
-  Persistence performance varies; can be better or worse than either
- 0 QPF or QPF.

## Conclusions

- Gridded output examples highlight two intrinsic benefits of the distributed hydrologic model – high resolution information and cell-to-cell routing.
- Initial examination of basin-event peak error vs. lead time cases (only a few examples shown here) indicate that:
- Use of 1 hour MPN-based QPF consistently improves upon assuming Zero** **QPF (MPN QPF rarely introduces more error than the Zero QPF case)
- Use of 1 hour Eulerian persistence often improves upon Zero QPF, but also shows worse results in a number of situations.  Persistence can produce better or worse results than QPF.