Patrick Broxton (University of Arizona)Michael Schaffner (National Weather Service)

Peter Troch (University of Arizona)Dave Goodrich (USDA–ARS–SWRC)

Carl Unkrich (USDA–ARS–SWRC)

Development of a Distributed All-Season Flash-Flood Forecasting System

1

Sixth Southwest Hydrometeorology Symposium

September 28, 2011

• Goals of current efforts

- Process based- Distributed- Continuous- Easily Calibrated

- Develop a modeling system to account for overland flow, catchment wetness, and snow

• Improve upon existing Flood and Flash Flood Forecasting Models

Introduction

• Leverages the KINEROS and SM-HSB models

- Both models are distributed with high spatial and temporal resolutions

- KINEROS is an event model that describes overland flow and channel routing (Kinematic Wave Routing)

- SM-hsB is a continuous model that describes snow (Energy Balance Model), soil moisture (Root Zone Model), and subsurface flow (hillslope-Boussinesq Model)

Shortwave Radiation

Longwave Radiation

Snow

Runoff

Channel Routing

Precipitation

Evapotranspiration

Canopy Interception

Infiltration

Baseflow

Model Description

- Baseflow, runoff (infiltration excess and saturation excess) from SM-hsB are routed using KINEROS

- Distributed as standalone, freely distributable, executables (created using Matlab Compiler Runtime) to perform the following:

• Programming in Matlab (HSB) and Fortran (KINEROS)

Visualize Results

Real-Time Modeling

Process Spatial Data

1) Assimilate Past Hydrometeorologic Data

2) Calibrate Model

1) Assimilate Real-Time Data

2) Run Real-Time Model

Past Simulations for Calibration/ Evaluation

As a webpageAs timeseries’ of model states

Model Description• KINEROS/SM-hsB is more than just a model, but an integrated system that

ingests data, runs the KINEROS and hsB models, and visualizes results

Processing Spatial Data

Delaware River Walton

Delaware River Delhi

Town Brook

East Brook

Platte Kill

-75.25 -75.125 -75.00 -74.875 -74.625-74.75 -74.50

-42.125

-42.25

-42.50

-42.375

Longitude (deg)

Latit

ude

(deg

)

Legend

Delineated StreamWatershed BoundaryDelineated HillslopeModel Grid Cell

USGS Stream Gauge

• Model has been set up to run in the W. Br. Delaware Basin, located in the Catskill Mountains of southeastern New York

Spatial Data from USGS: - DEM - Canopy Coverage Map - Vegetation Type Map - Impervious Surface Map

Spatial Data from NRCS: - SSRUGO soils map

GIS Preprocessing - Slope Map - Aspect Map - Locations of WSR-88D Radar Bins - Watershed Discretization (AGWA)

• Spatial data is either downloaded from the internet (e.g. DEM), generated using GIS (e.g. aspect map), or generated within KINEROS/SM-hsB (e.g. weighting scheme to go back and fourth between grid cells and hillslopes)

• Automated Geospatial Watershed Assessment Tool used to discretize watersheds

Past Simulations for Calibration/ Evaluation• Forcing data is from the National Land Data Assimilation

System (NLDAS) and the Multisensor Precipitation Estimator (MPE).

- NLDAS provides Temperature, Pressure, Incoming Radiation (Shortwave + Longwave), Precipitation Estimates, humidity at 1/8 degree. Derived from NARR reanalysis, Bias corrected radiation, gauge-only precipitation

- MPE provides precipitation estimates using gauge + radar data on 4 km grid

• USGS Streamflow data to validate KINEROS/SM-hsB discharge estimates

• Snow water equivalent (SWE) estimates from the Snow Data Assimilation System (optional) to compare to snow model.- SNODAS data combines model estimates with ground, airborne, and satellite measurements of snow

Past Simulations for Calibration/ Evaluation

• Calibration occurs in various steps

- Automated baseflow analysis

- Subsequent calibration of snow, radiation, evapotranspiration, infiltration, and runoff parameters involves changing parameters a few at a time and iterating (optionally fully automatic, manual, or a combination of both).

Down-Slope

0 10 20 30 40 5010-2

10-1

100

101

102

103

104

Effective Time

Deep Aquifer

Base

flow

(mm

/day

) HSB Aquifer

Aquifer Height/Unit Storage Catchment Width

3000 km

5.0 mm

- Automated calibration of HSB model (to find characteristic response, which is applied to each pixel in a watershed individually)

Calibration Period Evaluation Period Summer Winter Summer WinterDelaware River (Walton) 0.87 0.73 0.58 0.59Delaware River (Delhi) 0.83 0.65 0.64 0.50Platte Kill 0.68 0.60 0.49 0.49East Brook 0.73 0.74 0.61 0. 56Town Brook 0.68 0.51 0.65 0.42

0 20 40 60 80 100 120 140 160 180 200

0 20 40 60 80 100 120 140 160 180 200

0

30

60

90

0

50

100

150

200

250

0

50

100

Day of Simulation

Day of Simulation

Aqui

fer H

eigh

t (m

m)

Aqui

fer S

tora

ge (m

m)

Aquifer Storage (m

m)

75th Percentile HSB50th Percentile HSB25th Percentile HSB75th Percentile Simple50th Percentile Simple25th Percentile Simple

Aquifer Heights along Width Function

HSB StorageHSB DischargeSimple StorageSimple Discharge

Real Time Modeling

- NDFD provides estimates of forecasted temperatures, humidity, precipitation, cloud cover (updated hourly)

• Forcing data is from the National Digital Forecast Database and the Multisensor Precipitation Estimator (MPE).

- MPE provides near real-time precipitation estimates (updated hourly)

• Data comparison using USGS streamflow and Recent Estimates of SWE (either observed or from SNODAS)

• Model Forecasts 24 hours in advance using NDFD quantities

• Assimilation of 1 km X 1 degree WSR-88D radar data (under development)

• Ability to update model states

Real Time Modeling• Real-Time hydrometeorologic data is automatically downloaded continuously

(NDFD) or when the model is run (MPE, Streamflow, SNODAS)

- Available model visualizations include historical model data (previous slide) current hydrograph with stage and flood information (bottom left), and distributed estimates of soil moisture (bottom right) and SWE (not shown)

-This real-time output is available online at: http://newims.hwr.arizona.edu/DelewareBasinModel/

• Model runs automatically every hour (using a task scheduler) utilizing newly downloaded hydrometeorologic data (can be manually run more frequently during events)

• Model output is transmitted over the internet using a web page