# Presentation: 698200

## Distributed Models and Flash Flood Forecasting 

- Lecture 6b

## Model Errors as a Function of Scale 

- Flash floods

- 260

- Distributed model (uncalibrated). Each point is an average peak flow error from approximately 25 events over an eight year study period Oct 1996-Sept.2004.

- Scaling relationship for an uncertainty index (Rq) from Carpenter and Georgakakos (2004) (secondary axis)

- Log-linear regression for distributed model data

- This study used observed data; confirms numerical experiments from previous studies

**Notes:**
A distributed hydrologic model can make computations at spatial and temporal scales consistent with flash flooding.  This is real data that verifies the theoretical studies.  
Model errors tend to increase at smaller modeling scales.  
Will increased model errors in small basins mask the benefits of making calculations at the appropriate scales? 
Problems with FFG:  If there is local rain, FFG should change.  If there is more recent rain, FFG should change.

## Distributed Model Improvement Compared to Lumped-model Based FFG on Small Basins

- Distributed Model Improvement Compared to Lumped-model Based FFG on Small Basins

-   Calculate maximum rainfall-to-FFG ratios for selected events based on mean areal rainfall and mean operational gridded FFG.
-   Compare rank correlations between rainfall-to-FFG ratios and observed flood peaks for 71 events from September 2000-November 2004.

- Results from Reed et. al. (2007)

**Notes:**
Shows you can gain if you properly account for spatial and temporal scale.  Calibration at different scales is not necessarily meaningful. 

The rank correlation between the 1 hr rainfall-to-FFG ratio and observed flow peaks is 0.45, between the 3 hr rainfall-to-FFG ratio and observed flow peaks is 0.46, and between the 6 hr rainfall-to-FFG ratio and observed flow peaks is 0.42.  The rank correlation between the uncalibrated distributed model flow peaks and observed flow peaks is 0.66.  This indicates that even an uncalibrated distributed model is a better predictor of flash flood severity than operational FFG. 

## State Update Frequency

- Yellow line: 1 hr FFG with hourly updating
- Pink line: 1 hour FFG with 12 hourly updating
- White line: 1 hour MAPX

- Cowleech Fork Sabine, R. at Greenville, TX (GNVT2)

-   Update frequency would impact a warning decision in this example.
-   This suggests the need for a WFO distributed hydrologic model (similar to site specific perhaps).

**Notes:**
Part of the gains on the last slide may be simply due to the update frequency.

During a 22 month simulation period (Jan 2002 – Oct 2003), 
hourly MAPX exceeds FFG for: 
16 hours with hourly updating
9 hours with 12 hourly updating

As expected, simulation experiments show that FFG gives much different results if updated at the same time scale as the duration of the FFG. 
For ATIT2
Lumped FFG hits = 2 using 12 hourly FFG
Lumped FFG hits = 5 using 1 hourly FFG 

## Distributed Hydrologic Model with Threshold Frequencies (DHM-TF)

- 14 UTC

- 15 UTC

- 16 UTC

- 17 UTC

- Average Recurrence Intervals Associated with the Maximum Forecasts (1/4/1998)

- Generated in hindcast mode using QPE up to the forecast time and 1 hr nowcast QPF beyond

- Average Recurrence
- Interval (Years)

**Notes:**
In addition to distributed models, need to consider the best way to define a warning threshold.
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.

## Threshold Frequency Approach

- Benefits
- Relative measure (easier to understand than CMS)
- Inherent bias correction
- Local information often available to help define thresholds (e.g. engineering design, geomorphology studies)
- Can be used when ‘bankfull’ flow is not meaningful (e.g. slot canyons)
- Limitation
- Required data may not be available everywhere

## Ungauged Flash Flood Forecasting:  Moving Beyond FFG

- GIS-based Parameterization
- Soils, slopes, landuse

- Prototype hydrologic techniques
-   Gridded-SAC w/ Kinematic Routing (HL-RDHM)
-   GFFG
-   FFPI

- Warning thresholds
-   Threshold runoff (TR)
-   Threshold frequency (TF)

- Local knowledge; drainage design standards;
- flood stage data; geomorphology data; human impacts data

- Hydrologic
- guidance

- Threshold
- calibration

- Validation

## Needs

- Develop better validation and verification tools and databases
- Choose the best techniques for further development (may be regional differences)
- Choose the best resolution and update frequency for implementation
- Track success and understand the connection to GPRA goals
- Decouple the hydrologic model and the warning threshold
- Allows for easy local adjustment and “calibration” of warning thresholds
- Provide guidance on where data are adequate to support various techniques
- Integrate techniques with forecast precipitation (e.g. MPN) to improve lead times
- Improve statistical techniques for DHM-TF approach

## Real-time prototype
National/large area retrospective model runs
Validation of modeled flows at numerous small stream USGS stations (treated as ungauged)
Gridded verification with retrospective runs

- National/large area retrospective model runs
- Validation of modeled flows at numerous small stream USGS stations (treated as ungauged)
- Gridded verification with retrospective runs

- How to Meet the Needs

## Proposed HL-RDHM-TF/MPN Prototype

- HL-RDHM/MPN case studies showed that use of 1-hour QPF produces better simulated flow peaks than ‘0 QPF’ and ‘Persistence’ relative to a ‘Perfect QPF’ scenario
- Case studies using warning times from actual events suggest a potential for increased lead times
- HL-RDHM-TF/MPN real-time prototype benefits
- Determine if the concept will work for WFOs
- Further evaluate real-time forcing data quality and data latency issues (radar-gauge-satellite QPE, radar only QPE, short-term QPF)
- Evaluate higher temporal and spatial resolution forcing data
- Provide a framework to evaluate model enhancements

**Notes:**
Is a longer record length needed for small scales
How can we deal with the inherent observation noise at small scales

## Proposed HL-RDHM-TF/MPN Prototype Focus Area

**Why here ?**
-  Well-studied, high profile, flash flood prone area
-   EMPE/MPN prototype already running in this region
-   High resolution products are available here (1 km, sub-hourly)
-   Large number of stream gauges are available for validation (41 in 3500 km2 area)
-  Can compare with MARFC new gridded FFG approach

## Large Area Studies with HL-RDHM

-   Benefit flash flood, water resources, and river forecast applications
-   Quickly identify data problems
-   Assess applicability to many areas; provide guidance for implementation
-   Take full advantage of flash flood verification archives and USGS small station data
-   Improve models and parameterizations

- Surface Runoff (mm)

- Soil Saturation (mm)

- 1 hour rainfall (mm)

- 4/24/2004 7z Snapshots

## Historical

- Real-time or HindCast

- Use to update states once per day.

- HL-RDHM
- Sac, rutpix9 or rutpix9loc, AnnPeaks

- Max forecast peak flows
- (local or cell-to-cell

- HL-RDHM
- Sac, rutpix9 or rutpix9loc, FreqParams, HindCast, localFFG

- Max forecast peak frequencies
- (Local or cell-to-cell)

- GRASS

- Web

- Local FFG

- HL-RDHM Modules Facilitate Retrospective and Real-time Case Studies

**Notes:**
Main points:
HL-RDHM provides a useful framework for research.
Project will primarily be accomplished by creating user defined modules.
The modularity enforced by HL-RDHM and flexible experimentation should simplify the eventual transition to operational software.