# Presentation: 649787

## Future Science Developments in NWSRFS

- <date/time>

- Future Science Developments in NWSRFS

- Mike Smith
- NWS/OHD/Hydrology Lab
- October 21, 2003

- michael.smith@noaa.gov

## Goals of New Science

- Improve accuracy
- Quantify uncertainty

## New ScienceIncrease Accuracy  Quantify Uncertainty

- Increase accuracy
- Distributed modeling
- Improved snow & frozen ground modeling
- Improved estimates of precipitation, temperature, evapotranspiration
- Advanced hydraulic routing procedures
- Advanced parameter estimation/calibration procedures
- VAR data assimilation

## New ScienceIncrease Accuracy  Quantify Uncertainty

- Quantify Uncertainty
- Short term ensemble forecasting
- Use of longer term forecast forcings
- Major science questions

## What is a distributed hydrologic model?

- <date/time>

- What is a distributed hydrologic model?

- -a model which accounts for the spatial variability of factors affecting runoff generation:
- precipitation
- terrain
- soils
- vegetation
- land use
- channel shape

## Lumped 

- <date/time>

- Rainfall, properties averaged over basin
- One rainfall/runoff model
- Prediction at only one point

- Rainfall, properties in each grid
- Rainfall/runoff model in each grid
- Prediction at many points

- Lumped

- Distributed

- Hydrologic Modeling Approaches

**Notes:**
Parameters of lumped model represent entire yellow area.  Lumped approach averages the factors that affect the generation of streamflow. 

## Benefits of Distributed Modeling

- <date/time>

- 1. Finer Scale Modeling Can Lead to Better Results
- Ability to Model/Predict Processes in Basin Interior  *Flash Flood*
- Land Use Change Analysis
- Parameterization Simplified using GIS Data

**Notes:**
1.  Hypothesis: modeling of individual processes at each grid will lead to better outlet simulations.  HL is testing this with its distributed model. 
2.  Distributed models have the capability to predict processes at points in the interior of the basin.  This feature could potentially be used for flash flood modeling.
  Land use changes can be easily incorporated.
  Many data sets available for use in deriving parameters for distributed models. 

## Which distributed model to use?

- <date/time>

- Strategy involves:
- HL Distributed Model
- Distributed Model Intercomparison Project (DMIP)

**Notes:**
Distributed models are the future; However, there is not a clear path in scientific literature to the type of model for NWS river forecasting. So, HL has initiated a strategy:
Internal research:  HL will start developing its own model to get experience now. HL distributed model is modular, so we can easily incorporate DMIP results. 
 External research:  HL organizing DMIP to learn the benefits of models developed by academic and federal institutions.

## Hydrology LabDistributed Model(HL-Research Modeling System  HL-RMS) Project Leader: Victor Koren

- <date/time>

- Hydrology LabDistributed Model(HL-Research Modeling System  HL-RMS) Project Leader: Victor Koren

- Modular, flexible modeling system
- Gridded (or small basin) structure
- Independent rainfall-runoff calculations for each grid cell (SAC-SMA)
- Grid to grid routing of runoff (kinematic)
- Channel routing (kinematic)

**Notes:**
The HL Distributed Model is modular so that improvements can easily be added.
Current version of the model uses the Sacramento Model to convert rainfall to runoff.

## Sacramento Soil Moisture

- Accounting Model

**Notes:**
Explicit soil rainfall/runoff modeling on each computational element. 

## Treatment of SAC-SMA runoff components

- Fast runoff components
-  Surface
-  Direct
-  Impervious

- Slow runoff components
-  Interflow
-  Supplemental baseflow
-  Primary baseflow

- Hillslope routing

- Channel routing

**Notes:**
Computational elements linked to rout the different runoff components. 

Drainage density depicted ~ 1.1

Drainage density illustrated is similar to that from 1:100,000 scale USGS maps.

Slope of the main channel significantly different than tributaries.  Easy to derive for a basin – slightly more complicated for irregular grid cells.

## Conceptual Channels

- Drainage Density Illustrated
-  ~ 1.07

**0.0019**

- 0.08

- 0.009

- 0.007

- 0.01

- Actual Channel
-  Network

- Conceptual Channel
- Network

**Notes:**
Drainage density depicted ~ 1.1

Drainage density illustrated is similar to that from 1:100,000 scale USGS maps.

Slope of the main channel significantly different than tributaries.  Easy to derive for a basin – slightly more complicated for irregular grid cells.

## Example A-Priori Soil Parameter Grid

- UZTWM

**Notes:**
Initial experience with Tahlequah is that initial simulations are in the right ball park, but it is pretty easy to get improvement the calibration with some multiplicative factor adjustment.

## Application of HL Distributed Model

**Notes:**

These figures illustrate the processes performed on each grid:  blue river, 476 sq. miles, 80 cells.
Upper Left:  Each cell has its own unique parameters
 Upper Right:  Each cell has its own precipitation input
 Lower Left:  Each cell generates runoff
 Lower Right:  Runoff flows from cell to cell to the outlet.

## Hydrologic Response at Different Points in

**Hydrologic Response at Different Points in**
**the Blue River Basin **

**Hydrograph at Location A**

**Hydrograph at Location B**

**Hydrographs at Basin Outlet**

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**Flow (CMS)**

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**Flow (CMS)**

***B***

***A***

- Distributed
- Lumped
- Observed

- Flow (CMS)

**Notes:**
Title of talk mentions ‘improved river forecasting’  This slide illustrates two types of  improvements.

A distributed model can be used to simulate processes at points inside the basin. The lumped model is only able to give a simulation at the basin outlet. 
In this example, very little rain fell on the upper part of the basin.  There is no streamflow response at point A.  Small amounts of rain fell in the middle of the basin, so the streamflow does show a small response at point B.
The heaviest rain fell in the lower part of the basin. The lumped simulation shows the effects of averaging over the whole basin.  It underestimates the peak and is delayed.   The distributed model is able to capture the spatial variability of the precipitation and produce a more reasonable simulation. 

## Large Area Application 

- ABRFC

- Large Area Application
- of HL Distributed Model

*outlet*

- 25,000
- computational
- elements

## Distribution of Upper Zone Free Water

**(mm)**

## Distributed Model Intercomparison Project-  DMIP(First) Broad Comparison of Lumped and Distributed Models

- <date/time>

- Distributed Model Intercomparison Project-  DMIP(First) Broad Comparison of Lumped and Distributed Models

- Sponsorship: NWS OHD/HL and GCIP/GAPP
- Results used to guide NWS future research into finer scale modeling
- Participation from universities, other Federal agencies and internationally

## Why is DMIP Needed?

- <date/time>

- Not a clear path to distributed model for NWS
- Hypothesis: high resolution data will lead to better results
- NWS needs scientific community to help guide its research
- To understand the value of NEXRAD and spatial data to improve river forecasting
- To provide a venue to compare complex models to those used in operational forecasting

**Notes:**
Hypothesis needs to be tested
Need a comparison of distributed vs. lumped models

## DMIP Elements

- <date/time>

- HL provided data sets for test basins on DMIP web site
- Participants accessed data, generated simulations
- HL compared simulations to NWS lumped and distributed model runs
- HL hosted summary workshop

**Notes:**
Workshop
 Statistical evaluation of participant’s simulations
 Discussion of model structure, parameterization
 Publish results
4.  Next phases.

## DMIP Study Basins

- 1

- 2

- 3

- 4

- 5

- 6

- Arkansas R.

- Red R.

- Blue R.

- Elk R.

- Ill. R. at Tahlequah, OK
- Baron Fork at Eldon, OK
- Peacheater Creek, OK
- Flint Creek at Kansas, OK
- Ill. R at Watts, OK
- Ill. R at Savoy, OK

## DMIP Data Sets

- NEXRAD hourly 4km. estimates (’93-’00)
- DEM data
- Cross sections
- Meteorological data
- Observed hourly flow data
- Soils information
- Vegetation

## DMIP Participants

- <date/time>

- MIT
- NCEP/EMC
- HL
- U. of Ariz.
- HRC
- DHI
- U. of Ok.

- Blacklands Research Center
- Utah State U. and NIWA, New Zealand
- Wuhan U., China
- U. of Waterloo, Canada
- UC Berkeley

**Notes:**
Like to get Fred Ogden at U. Conn. And the Hydrologic Engineering center to participate in the future.

Models ranged from conceptual to complex physically based

## DMIP Schedule

- <date/time>

- March 2001 – Data sets were available
- March 31,  2002 – All simulations were due to HL
- August 21-23, 2002 - Workshop: discussed of results, publications,  and future DMIP phases
- Current: Publication of results in Special Issue of Journal of Hydrology

## DMIP Results

- basin outlets

- interior points

**Notes:**
We generated a large number of comparison statistics.  I will present a few overall results.

## DMIP Results

- interior points

- basin outlets

## DMIP Overall Results

- Proved that for certain events and basins, distributed models provided benefit compared to lumped.
- Demonstrated that reasonable hydrographs can be predicted at interior points.
- Exposed research models to operational data and concerns.
- Acknowledged that DMIP is a powerful method to get science from research community to operations.
- Showed need to understand data errors and streamflow.
- Established that OHD/HL distributed model research is very competitive.

## Challenges of Distributed Modeling

-                             Space/Time Variability
- Does accounting for the space/time variability of input data and parameters guarantee better results?

- Effect of noisy rainfall data on the
- peak volume at different simulation scales.

- Increasing model resolution

- Error

- 25%

- 75%

- 50%

- 0%

- Koren et al., 1999, Scale dependencies of hydrologic models to spatial variability of precipitation , Journal of Hydrology

**Notes:**
Continue DMIP work with 
Better defined noise (spatially correlated errors)
Compare to observed data. 

## Future Phases of DMIP

- Basins in other climatic regimes
- Snow
- Orographics
- Other data for verification
- Soil moisture
- Nested basins
- Synthetic simulation analyses

## Snow Modeling  SNOW-17, Energy Based, and Distributed Snow Modeling

## Motivation

- Improve performance of SNOW-17
- Make possible to run it in a distributed mode
- Investigate performance of energy-budget snow models in quasi-operational setting

## SnowMIP results for Sleeper River (USA) site: 23 energy based 

**SnowMIP results for Sleeper River (USA) site: 23 energy based **
**models and SNOW-17 (Uncalibrated model results).**

## Intermediate Snow-17 Modifications

-    Addition of snow depth simulations
-    Dynamic wind function
-    Liquid water refreezing formulation

## OHD PLANS

-    Distributed snow modeling
-    Include SNOW-17 into HL-RMS to make it available for RFC
-      tests
-    Work on a grid type SNOW-17 parameterization
-    Energy based snow modeling
-    Collect and analyze new data sources to run an energy          	based snow model over US
-    Analyze dependency of simulation results from energy and
-      SNOW-17 models
-    Define the most reliable data sources to run an energy snow 	model in the HL-RMS setting
-    We expect to hire a person to perform data and comparison
-      analyses

## Frozen Ground Modeling Efforts

- Existing parameterization
- Need in more physical approach to describe heat fluxes
- Large number of parameters
- Requirements for new method
- Simple enough procedure to run with limited noisy operational data
- Procedure should be compatible with SAC-SMA complexity

## Existing Frozen Ground Model

**SAC-SMA frozen ground component has two parts**
**Frost index calculation component**
**Modification of the water balance using frost index**
**Frost index component**
**Mimics Stefan’s solution for freezing depth**
**Accounts conceptually for snow depth**
**Has four empirical parameters:**
**C****g**** – bare ground frost coefficient**
**C****s**** – reduction in C****g**** due to snow pack**
**H****c**** – daily thaw rate from ground heat**
**C****t**** – thaw coefficient for water entering the soil**

## Existing Frozen Ground Parameterization

**Modification of the water balance (percolation and interflow reduction) using frost index**
**Mimics empirical relationship between losses reduction and soil saturation and freezing depth**
**Uses threshold type dependency on the frost index**
**Has three empirical parameters:**
**FI****L**** – frost index threshold**
**C****r**** – water withdrawal reduction under saturated conditions**
**x – exponent of lower zone soil moisture deficiency ratio**

## Linking of soil moisture (SAC-SMA) and heat states

**Linking of soil moisture (SAC-SMA) and heat states**

## Frost Index Replacement

-    **N-layers soil column**
**   ****The layer-integrated form of diffusion equation**
**   ****Soil moisture and heat fluxes are simulated separately at each time step**
**   ****Surface temperature is equal to air temperature**
**   ****Lower boundary is set at the climate annual air temperature**
**   ****Unfrozen water content is estimated as a function of soil temperature, **
**      ****saturation rate, and ice content**

## River Mechanics

- Routing of large rivers
- Tidal effects
- Flood inundation mapping
- Dam failure analysis
- Sediment transport

## Recent Enhancements to FLDWAV Model

- Stand-Alone model
-   - Ability to model channel networks including bifurcations
-    - Ability to handle floodplain compartments (network + levee options)
-    - Ability to model mudflows
-    - Ability to export information for FLDVIEW and FLDAT applications
-    - Muskingum-Cunge Routing method
- Operational model
-   - Added ability to blend observed and NOS tide time series
-    - Added ability to blend observed adjust computed stage time series
-    - Tested several internal boundary options including rating curves &
-       bridges
-    - Ability to model channel networks including bifurcations
-    - Ability to handle floodplain compartments (network + levee options)
-    + Ability to export information for FLDVIEW and FLDAT applications

## Flood mapping

## Future Enhancements to FLDWAV Stand-Alone Model

- Add new capabilities
-     + Sediment transport
-      + Pollutant transport
-      + Routing flow thru culverts
-      - Channel losses
-      - Practical Updating Techniques
-      - Ice Jams
- Add DWOPER/DAMBRK capabilities not currently in FLDWAV
-     - Landslide generated waves
-      - Multiple movable gates
-      - Improved method of modeling wind effects

## MOPEX(Model Parameter Estimation Experiment)GOALS

- Develop improved methods for a priori estimation of model parameters for hydrological and hydrometeorological models operating at space (100-100,000 km2) and time scales (10 min to 1 day) appropriate for a range of applications including river forecasting and water resources management, weather forecasting, climate prediction, and climate change study.

## MOPEX Participating Models 

- Simple Water Balance - NWS
- Sacramento - NWS
- ISBA
- SWAP – Gusev/Nasonova
- VIC – Princeton/Washington
- USGS - PRMS
- Sacramento – Thian Yew Gan, U. Alberta Canada
- NOAH – NWS
- Kuni Takeuchi - Japan

## Data Assimilation

- Bring in all available data (observations, estimates, model output, etc.) into the hydrologic model while maintaining dynamic consistency of the physical processes being modeled
- Improves forecast by reducing errors in the initial and boundary conditions
- Gives a better handle on uncertainty, scale

## Assimilation: Background

- Two biggest hurdles to objective operational hydrology
- Model calibration
- Run-time modification (MOD)
- (Semi-) Automatic run-time modification proven difficult

## Assimilation: Background (cont.)

- Significant issues with SS-SAC recognized
- Variational assimilation (VAR) approach proposed (May 2000)
- 1st-yr summary presented (Sep 2001)
- Directed to implement the prototype at a forecast office
- Collaboration with WGRFC started (Dec 2001)

## VAR: Current Status (cont.)

- WGRFC started running the VAR operation for 6 headwater basins (Jan 2003)
- 1-hour time step
- RFC (6-hour) SAC parameters
- 1-hour empirical UH

## VAR: Near-term plan

- Refine the empirical UH estimation technique
- Purely empirical
- (Using geomorphological UH as a priori)
- The computer code is to be delivered to WGRFC by May 1, 2003

## VAR: Near-term plan (cont.)

- Develop a technique and a computer code for automatic adjustment/refined of SAC parameters
- Using the RFC operational as a priori
- Using the soils-based as a priori
- SCE-based
- Explicit gradient (adjoint)-based
- (Hybrid approach)

## Probabilistic/Ensemble Hydrologic Forecasting

- A new paradigm in hydrologic forecasting
- To account for uncertainties in;
- boundary conditions (e.g., observed and forecast precipitation)
- initial conditions (e.g., soil moisture)
- model errors (including parameter uncertainty)
- Quasi-analytical vs. numerical

## Ensemble Streamflow Prediction (ESP)

## Slide 54

## Existing Capability to Produce Ensemble Precipitation Forecasts

- NWSRFS includes techniques to create ensemble products from existing NWS forecasts
- All time scales from the next few hours up to seasonal to inter-annual

## Emerging Capabilities - Weather and Climate Ensemble Forecast Applications

- Simplified short term ensembles using existing deterministic QPF (1-5 days)
- Medium range global ensembles (<2wks) (NCEP and ECMWF)
- Short range regional ensembles (<2days)
- Long range coupled ocean – land –atmosphere ensembles (>2wks)

## Short Term Ensemble Precipitation Forecasts from Deterministic Forecasts

- Analyze joint distribution of historical forecasts and corresponding observations
- Estimate joint distribution parameters
- Derive conditional distribution given a deterministic forecast – varies spatially
- Create synthetic ensemble forecasts consistent with conditional distribution
- Evaluate results

## Questions

- Utilization of ENSO signals
- Re-/down-scaling of boundary conditions
- Probabilistic characterization of errors in initial conditions and model parameters
- Sample size
- Extreme values
- Validation of probabilistic/ensemble prediction

## Thank you!

**For more information - http://www.nws.noaa.gov/oh/hrl**