# Presentation: 066117

## Ensemble streamflow forecasting in

- snowmelt-dominated river basins:
- Martyn P. Clark
- CIRES, University of Colorado at Boulder

- Funded by the GEWEX Americas Prediction Project (GAPP)

- _Collaborators:_
- David Brandon
- Soumya Chennapragada
- Subhrendu Gangopadhyay
- Lauren Hay
- Yeonsang Hwang
- George Leavesley
- Balaji Rajagopalan
- Pedro Restrepo
- John Schaake
- Andrew Slater
- Kevin Werner

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

- Run hydrologic model up to the start of the forecast period to estimate basin initial conditions

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

- Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
- Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

- Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
- Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.

## Integrated set of forecast inputs (days—seasons)...

- (different models for different forecast lead times)
_**Short-term:**_  Bias-corrected output from a regional model
- 	- 72-hour forecasts from the regional reanalysis
_**Medium-Range**_**:**  Downscaled output from a global forecast model
- 	- 14-day forecasts from the CDC frozen version of NCEP’s 			MRF model
_**Seasonal time scales:**_  Dis-aggregated probabilistic forecasts
- 	- weather generator conditioned on climate indices
- 	- weather generator conditioned on probabilistic forecasts

## Integrated set of forecast inputs (days—seasons)...

- (different models for different forecast lead times)
**Short-term:**  Bias-corrected output from a regional model
- 	- 72-hour forecasts from the regional reanalysis
**Medium-Range:**  Downscaled output from a global forecast model
- 	- 14-day forecasts from the CDC frozen version of NCEP’s 			MRF model
**Seasonal time scales:**  Dis-aggregated probabilistic forecasts
- 	- weather generator conditioned on climate indices
- 	- weather generator conditioned on probabilistic forecasts
**Requirements:**
- 	- an ensemble *daily* sequences of weather
- 	- preserve inter-site correlations, temporal persistence, and
- 		correlations between variables
- 	- minimize abrupt changes when a new model is introduced

## Integrated set of forecast inputs (days—seasons)...

- (different models for different forecast lead times)
**Short-term:**  Bias-corrected output from a regional model
- 	- 72-hour forecasts from the regional reanalysis
**Medium-Range:**  Downscaled output from a global forecast model
- 	- 14-day forecasts from the CDC frozen version of NCEP’s 			MRF model
**Seasonal time scales:**  Dis-aggregated probabilistic forecasts
- 	- weather generator conditioned on climate indices
- 	- weather generator conditioned on probabilistic forecasts
**Requirements:**
- 	- an ensemble *daily* sequences of weather
- 	- preserve inter-site correlations, temporal persistence, and
- 		correlations between variables
- 	- minimize abrupt changes when a new model is introduced

## PRECIPITATION BIASES

**Precipitation biases are in excess**
**of 100% of the mean**

## TEMPERATURE BIASES

**Temperature biases are in excess**
**of 3****o****C**

## Downscaling approach 

**For hydrologic applications we need to:**
**Obtain reliable local-scale forecasts of precipitation and temperature**
**Preserve the spatial variability and temporal persistence in the predicted temperature and precipitation fields**
**Preserve consistency between variables**
**Multiple linear Regression with forward selection**
**	****	****Y**** = ****a****0**** + ****a****1****X1**** + ****a****2****X2**** + ****a****3****X3**** . . . + ****a****n****Xn ****+**** ****e**
**A separate equation is developed for each station, each forecast lead time, and each month.**
**Use cross-validation procedures for variable selection – typically less than 8 variables are selected for a given equation**
**Stochastic modeling of the residuals in the regression equation to provide ensemble time series**
**Shuffling of the ensemble output to preserve the observed spatial variability, temporal persistence, and consistency between variables. **

- Clark and Hay, 2004

## BASINS

**BASINS**

**Compare ESP and SDS 9-day forecasts of runoff every 5 days**

## Alapaha River Basin (Southern Georgia) 

- Clark and Hay, 2004

## Animas River Basin (Southwest Colorado) 

**Animas River Basin (Southwest Colorado)**

- Clark and Hay, 2004

## Cle Elum River Basin (Central Washington) 

**Cle Elum River Basin (Central Washington)**

- Clark and Hay, 2004

## Carson River Basin (CA/NV Border) 

**Carson River Basin (CA/NV Border)**

- Clark and Hay, 2004

## The CDC Re-forecast experiment

** ****CDC are continuing to run the 1998 NCEP MRF in real time.**
** ****The NWP hindcast (1979-2001) is used to develop regression models between MRF output and precipitation and temperature at individual stations, and apply the regression coefficients to the CDC experimental forecasts in real-time.**
** ****The resultant local-scale precipitation and temperature forecasts are used as input to the CBRFC hydrologic modeling system to provide real-time forecasts of streamflow.**

**The CDC Re-forecast experiment**

- Werner et al., in press

## Operational Implementation (NWSRFS) 

**Operational Implementation (NWSRFS)**

- 	Hourly instantaneous flow ensembles are created by ESP and saved. MRF shows higher flows than historical when it is warmer (during the first week). These may be converted into probabilistic forecasts...

- Werner et al., in press

## Ranked Probability Skill Score (RPSS)

**Ranked Probability Skill Score (RPSS)**

**Improvements in forecast skill most pronounced during the rising limb of the hydrograph.**

**(20-year hindcast)**

- Werner et al., in press

## Integrated set of forecast inputs (days—seasons)...

- (different models for different forecast lead times)
**Short-term:**  Bias-corrected output from a regional model
- 	- 72-hour forecasts from the regional reanalysis
**Medium-Range:**  Downscaled output from a global forecast model
- 	- 14-day forecasts from the CDC frozen version of NCEP’s 			MRF model
**Seasonal time scales:**  Dis-aggregated probabilistic forecasts
- 	- weather generator conditioned on climate indices
- 	- weather generator conditioned on probabilistic forecasts
**Requirements:**
- 	- an ensemble *daily* sequences of weather
- 	- preserve inter-site correlations, temporal persistence, and
- 		correlations between variables
- 	- minimize abrupt changes when a new model is introduced

## The weather generator model... (seasonal predictions)

- Identify a subset of years from the historical record, such that the CDF from the selected years matches the CDF from the probabilistic forecast

**January**

- 1982
- 1992
- 1997
- 1952
- 1967
- 1979
- 1982
- 1990
- 1994

_**For 16**__**th**__** January**_, select an ensemble of data from a biased set of years

**x**

**x**

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## The weather generator model... (seasonal predictions)

- Identify a subset of years from the historical record, such that the CDF from the selected years matches the CDF from the probabilistic forecast
- Re-sample data from the subset of years nens times

**January**

- 1982
- 1992
- 1997
- 1952
- 1967
- 1979
- 1982
- 1990
- 1994

_**For 17**__**th**__** January**_, select an ensemble of data from a biased set of years

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

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**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

- ...and so on...

## The weather generator model... (seasonal predictions)

- Identify a subset of years from the historical record, such that the CDF from the selected years matches the CDF from the probabilistic forecast
- Re-sample data from the subset of years nens times
- Re-order the ensembles to preserve observed inter-site correlations, observed temporal persistence, and observed correlations between variables

**January**

- 1982
- 1992
- 1997
- 1952
- 1967
- 1979
- 1982
- 1990
- 1994

_**For 17**__**th**__** January**_, select an ensemble of data from a biased set of years

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

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**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

- ...and so on...

## El Nino

**El Nino**

**La Nina**

**Conditioning on CPC forecasts**

- Clark et al., 2004b

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

- Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;
- Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.
- (_problem_:  approach does not account for uncertainty in model estimates of basin initial conditions, or for uncertainty in the hydrologic model itself)

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

**Aim:  Develop methods to account for uncertainty in all aspects of the streamflow forecasting problem**
**Modeling uncertainties:**
**Model Forcing**
**Model Structure**
**Model parameters**

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

**Aim:  Develop methods to account for uncertainty in all aspects of the streamflow forecasting problem**
**Modeling uncertainties:**
**Model Forcing**
**Model Structure**
**Model parameters**

## Ensemble Model Forcing – 1989/1990

- Cumulative Precipitation

- Cumulative Precipitation

- (Grizzly Peak)

- (Willow Park)

- Clark and Slater, in prep

## Impact of stochastic forcings: The SNOW-17 Model

- A simple empirically based model
- Needs only Temperature and Precipitation
- Runs Operationally @ the NWS
- Parameters : from CBRFC operational code
- Calibrated for streamflow

- Impact of stochastic forcings: The SNOW-17 Model

## Model “Control” ResultsGrizzly Peak 1998/1999

- Truth is within the spread

- Model “Control” ResultsGrizzly Peak 1998/1999

- Clark and Slater, in prep

## Model “Control” ResultsWillow Park 1989/1990

- Truth is sometimes out
- of the spread
- We haven’t accounted for
- uncertainty within the
- hydrologic model itself

- Model “Control” ResultsWillow Park 1989/1990

- Clark and Slater, in prep

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

**Aim:  Develop methods to account for uncertainty in all aspects of the streamflow forecasting problem**
**Modeling uncertainties:**
**Model Forcing**
**Model Structure**
**Model parameters**

## Uncertainties with model structure

- (identical model forcings)

## Uncertainties with model structure

- (identical model forcings)

_**Problems:**_
- Models share assumptions and source code, and
- thus ensemble spread from a multiple models
- may not be a good proxy for model error.
- Cumbersome to implement a multi-model
- system and/or share operational model output
- between different forecasting groups.
- Work required to identify effective methods
- to combine output from multiple models

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

**Aim:  Develop methods to account for uncertainty in all aspects of the streamflow forecasting problem**
**Modeling uncertainties:**
**Model Forcing**
**Model Structure**
**Model parameters**

## Parameter Uncertainty—Monte Carlo Markov Chains 

- Select a random distribution
- 	of points in the feasible
- 	parameter space
- Perturb each point
- Calculate the ratio of the
- 	probability of the perturbed
- 	point and the original point
- If the ratio is greater than a
- 	uniform random label [0,1],
- 	then accept
- 5.  Keep going until converged

## Parameter Uncertainty—Monte Carlo Markov Chains 

- Select a random distribution
- 	of points in the feasible
- 	parameter space
- Perturb each point
- Calculate the ratio of the
- 	probability of the perturbed
- 	point and the original point
- If the ratio is greater than a
- 	uniform random label [0,1],
- 	then accept
- 5.  Keep going until converged

## Model-based solution to the 

- Model-based solution to the
- streamflow forecasting problem...

- State Updating

**Aim:  Develop methods to account for uncertainty in all aspects of the streamflow forecasting problem**
**Modeling uncertainties:**
**Model Forcing**
**Model Structure**
**Model parameters**

## Model “Control” Results Grizzly Peak 1998/1999

- Truth is within the spread

- Model “Control” Results Grizzly Peak 1998/1999

## 5-Day EnKF Assimilation Cycle Grizzly Peak 1998/1999

- Error is given
- “Truth”
- Spread becomes tighter
- With large observational
- errors, model follows its
- own course.

- 5-Day EnKF Assimilation Cycle Grizzly Peak 1998/1999

- Slater and Clark, in prep

## Results Comparison Grizzly Peak 1998/1999

- Slater and Clark, in prep

## Summary and Future Work...

- Have introduced methods to improve both local-scale hydrologic forecast inputs, as well as initial model states
- Downscaled Numerical Weather Prediction model output
- Dis-aggregated seasonal climate forecasts
- Snow data assimilation methods
- Current and future work is focused on uncertainties in hydrologic modeling systems – model complexity, parameter uncertainty, and structural modifications to improve the simulation of runoff processes.

**Historical Simulation**

**Q**

**SWE**

**SM**

**Historical Data**

**Forecasts**