# Presentation: 237771

## Ensemble River Flow Forecasting Forced by GEFS Runoff Forecasts:Preliminary Experiments

*Dingchen Hou*, Kenneth Mitchell, Zoltan Toth,*
*Dag Lohmann**  and Helin Wei**
- Environmental Modeling Center/NCEP/NOAA
- 5200 Auth Road Camp Springs, MD 20746
- * SAIC at EMC/NCEP/NOAA
- ** Risk Management Solution Ltd.
- Acknowledgement:
- Dongjun Seo, Pedro Restrepo and John Schaake, OHD/NOAA
- George Gayno, Yuejian Zhu, Jesse Meng and Youlong Xia,
-  EMC/NCEP/NOAA

## Current NCEP Routines (No Stream Flow Forecast)Operational GEFS real time              NLDAS quasi-real timeForecast (Ensemble)                            Analysis (Single)

**Background**
-    Land Surface component of NWP systems (Mitchell et al, 2004) facilitates stream flow forecast in NWP.
-    River routing experiment in analysis mode of the North American Land Data Assimilation System (NLDAS) project (Lohmann et al, 2004) revealed potential benefit of river flow forecast in NWP.
-    Existence of uncertainty in initial conditions, model structure and forcing needs to be considered with an ensemble approach.

##  Purpose and Strategy

**Purpose:**
- Demonstrate feasibility of gridded river flow forecast in operational GEFS.
- Establish optimal configuration for river routing.
- Develop suitable strategy to account for uncertainties.
- Develop suitable methods of post processing and generating final products.
- Validate model output of stream flow.
**General Strategy**:
- NLDAS stream flow analysis used as initial condition and verification;
- Water management issues avoided by focusing on natural flow forecast;
- Global domain in mind with domestic and international users;
- River flow forecast capacity as a component of the ESMF system;
- Hind cast data set to be generated for post processing.

## Ensemble Stream Flow ForecastTwo Possible ApproachesA)                                           B)  

## Configuration 

**Design of Preliminary Experiment (Approach A)**
**River Routing Model**: linear program, distributed approach,  same as used in NLDAS (Lohmann et al., 1998, 2004).
**CONUS domain**, 1/8 degree grid size (same as NLDAS).
**River Flow Direction Mask**: A D8 model, river stream in each grid point is discharged to 1 of the eight main directions (Lohmann, et al, 2004).
**Forcing**: Runoff from global ensemble forecasts (GEFS) and the high resolution control forecast (GFS), interpolated to NLDAS grid.
- Downscaling not considered yet
**Uncertainty** considered in river routing: only in the forcing, added partially.
- No initial uncertainty is included yet
- Hydrological model error is ignored but systematic model errors can be corrected via post processing
**Initial Conditions**: NLDAS Analysis.
**Evaluation**: For this preliminary test, natural flow is predicted and evaluated against NLDAS analysis, which is generated from observed precipitation.

## Forecast Example (initiated April 1st, 2006) Stream Flow Forced by GFS,GEFS Forecast and NLDAS ProductForecast Starting at 00Z, April 1st, 2006. Lead time 15 days

**GFS High Res. control**

**Ensemble (low res.) Control**

**Ensemble Mean**

**Analysis (NLDAS)**

- Single control
- forecasts similar
- to each other;
- Ensemble mean
- is similar to the analysis.
- This suggests
- the ensemble mean has its value in stream
- flow forecast.

## Forecast Example (initiated April 1st, 2006) Stream Flow  Stream Flow, Analysis and Ensemble Mean ForecastError of Ensemble Mean and Ensemble SpreadForecast Starting at 00Z, April 1st, 2006. lead time 12 days

**Error=Ens. Mean - analysis **

**Analysis (NLDAS**)

**Ensemble Spread**

**Ensemble Mean**

- Ensemble mean
- is similar to the
- Analysis;
- Geographic
- distribution of
- positive and
- negative errors.
- Spread and error are
- Comparable.
- Note the scales
- for error and spread is 1/10
- of that for analysis and
- Ensemble mean.

## May 4th

**Mississippi, River**
**Vicksburg, MS**
**The Large Basin**
**May 4****th**** case**
- A major mid-range event well   predicted
- Significant spread in
- extended range
**April 1****st**** case**
- With out  a major event,
- all simulations are similar and spread is small.
- Trend and events** **picked up.
- Short lead time dominated
-    by initial condition, showing
-    little spread.
- Spread Increases with time.

**May 4th**

**April 1st**

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

## Potomac River

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

**April 1st**

**May 4th**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

**Potomac River**
**A Medium Sized Basin**
**In both cases**
- Single forecasts are insufficient.
- Non-linear evolution of ensemble members help to improve forecast and catch major flood events.

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

**Merrimack-Concord River**
**Lowell, MA**
**A Medium Sized Basin**
**Major Problem**
- Underdispersive ensemble
- in grid and subgrid scale precipitation.
**Mid-May Flood Event**
- Compared with the Early-April event, the Mid-May event is harder for the model to simulate. Nevertheless, the ensemble shows some skill indicating a major event with 10+ day lead, various amplitude and timing.
- Major event is correctly forecast despite short range over- forecast and insufficient spread.

**May 4th**

**April 1st**

**----- GEFS members**
**----- GEFS ens. mean**
**----- GEFS control**
**----- GFS high resolution**
**----- NLDAS**

- 0           2              4             6             8            10           12           14           16
-                                        Lead Time  (days)

**April 1st**

**May 4th**

**Nehalem River, FOSS OR**
**A Small Basin**
- A challenge for the model.
- April 1st, large forecast discrepancy from day 1
- despite significant spread

**Possible causes of the problem in the short range forecast **
-  Lack of spread in precip. fcst. on grid and subgrid scale.
-  Spatial and temporal resolution of the runoff.
-  Bias of precipitation (and runoff) forecast

## Summary of Results

- Distributed river routing ensemble system (coupled GEFS, NOAH and the Lohmann River Flow model) works.
- Spread ~ Error of ensemble mean forecast
- Large basin forecasts look reasonable.
- GEFS provides reasonable forcing?
- Downscaling and river initial condition error do not matter?
- Medium/small basin forecasts suffer from initial underdispersion.
- Downscaling and initial perturbation needed.
- Systematic errors in hydrological models to be addressed via post processing using hind casts.
- Anomalous river flow forecasting.
- Uncertainty in hydrological model to be addressed via
- Multi model
- Post Processing (Bayesian and Frequentist)

## Background

## Forecast Example (initiated April 1st, 2006) Stream Flow  Stream Flow, Analysis and Ensemble Mean ForecastAbsolute Error of Ensemble Mean and Ensemble SpreadForecast Starting at 00Z, April 1st, 2006. lead time 12 days

**Analysis (NLDAS**)

**Ensemble Mean**

**Absolute Error **

**Ensemble Spread**

- Same as
- previous slide
- except for the
- error, where absolute value is
- plotted to compared with the spread.
- Spread is comparable to error, but the value is smaller, especially in the West.

##  Further Development----Issues

**General Strategy**
- Cooperation with HEPEX, NAEFS and THORPEX.
- Facilitate probabilistic forecasting.
**Configuration**
- Expand to global domain (Initially at 0.5 degree resolution).
- Simplified lumped approach.
**Coupled and Uncoupled Approaches**
**Coupled** (Approach A): A complete forecast system of the Atmosphere-Land Surface-River Routing System.
**Uncoupled **(Approach B): Facilitate bias correction of precipitation and separation the uncertainty in forcing and in the hydrological (land surface) model.
**Bias Correction**
- Precipitation for better input runoff.
- Stream flow output for better final product.
- via a hind-cast data set for a better estimate of bias (Toth).
**Representation of Uncertainties in**
- Precipitation (subgrid downscaling, stochastic perturbations).
- Land surface and river routing model (multi model or varying parameters).
- Initial state of “surface water” (how?).
**Evaluation**
- Quantitative evaluation with rigorous statistics (EMC).
- Consider the effects of human control and water management (users).