# Presentation: 065147

##  Drought Monitoring and Prediction Systems at the University of Washington and Princeton University 

- Climate Diagnostics and Prediction Workshop
- Lincoln, NW
- October 22, 2008

- Dennis P. Lettenmaier
- Department of Civil and Environmental Engineering
- University of Washington

## Outline of this presentation

- Motivation for experimental hydrological prediction systems
- Evolution of the UW and Princeton systems
- Current components
- UW west-wide prediction system
- UW surface water monitor
- Princeton eastern U.S. and CONUS systems
- Integration
- Outstanding issues

## Motivation for experimental hydrological prediction systems: Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)

**Motivation for experimental hydrological prediction systems: Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)**

## Slide 4

## Slide 5

## Snoqualmie River at Carnation, WA

**Flood of record**

**Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation**

**Snoqualmie River at Carnation, WA**

## Slide 7

## How important is calibration for seasonal hydrologic prediction?

**How important is calibration for seasonal hydrologic prediction?**

## How important is calibration:  ensemble mean (from ESP) vs obs for April-July forecasts on six forecast dates, Gunnison River, CO

- uncalibrated

- uncalibrated bias corrected

- calibrated

**How important is calibration:  ensemble mean (from ESP) vs obs for April-July forecasts on six forecast dates, Gunnison River, CO**

## Why do we need an experimental hydrological prediction system?

*Despite the potential benefits of improved hydrologic forecasts, most operational hydrologic prediction at seasonal lead times ... are based on methods and data sources that have been in place for almost half a century.*
*The skill of western U.S. seasonal streamflow forecasts has generally not improved since the 1960s.*
*While forecast accuracy improvements would likely result from observing system densification, the need for long data records in regression-based methods would take decades to realize, and would be complicated by a changing climate.*
*We believe that a more promising pathway lies in the development of methods ... for assimilating new sources of observational data into land surface energy and water balance models, which can then be forced with modern climate and weather forecasts.*

**Why do we need an experimental hydrological prediction system?**

## One reason for the slow progress in hydrologic prediction has been the lack of real-time testing of new prediction models and methods ...

## The need for a national perspective on hydrologic prediction

**Will help to address emerging water resources operation and planning issues (e.g., nonstationarity)**
**Better exploit predictability in weather and climate (which is inherently at progressively larger scales with lead time)**
**Make better use of methods, like data assimilation, that can use large scale data sources to improve hydrologic initial conditions**

## Evolution of the UW and Princeton (near) real-time hydrologic forecast systems

- From Wood et al (2002) – development of a hydrologically based statistical downscaling method

## GSM Regional Bias:a spatial example

- Bias is removed at the monthly GSM-scale from the meteorological forecasts
- (so 3rd column ~= 1st column)

**Notes:**
important point(s):
here we see the biases from a spatial view -- note the large temperature bias in raw GSM forcing (for July), and in precip over the eastern edge of the Ohio Basin (as an example).  In the third column of images, the biases have been removed by our method, so they should look like the first column (observed) – and they do.

## Downscaling Test

- Start with GSM-scale monthly observed met data for 21 years
- Downscale into a daily VIC-scale time series
- Force hydrology model to produce streamflow
- Is observed streamflow reproduced?

## Simulations

**Notes:**
important point(s):
after bias correcting and downscaling the climate model forecasts, the procedure for producing hydrologic forecasts is as follows:
we spin up the hydrologic model to the start of the forecast using observed met. data (from 2 sources:  NCDC cooperator stations through 3-4 months before the start of the forecasts, then LDAS 1/8 degree gridded forcings thereafter). 
The GSM forecasts comprise 2 sets of ensembles, one for climatology and one for the forecast.  The climatology ensemble yields a distribution of the conditions we’ve seen over the period 1979-1999, while the forecast ensemble yields the distribution of the conditions we might see for the next 6 months.  Although the climatology ensemble is nominally unbiased against a simulated climatology based on observed met. data (rather than bias-corrected, downscaled GSM met. forcings), we compare the forecast and GSM climatology so that any unforeseen biases (resulting, perhaps, from the downscaling method) occur in both climatology and forecast.  Eventually this cautionary step may be eliminated, and we’ll compare directly to the simulated observed climatology.
at the end of the spin-up period and one month before month 1 (out of 6) of the forecasts, we save the hydrologic model state.  The state is then used for initializing the forecast runs.  Through the first month, the model runs on observed data to the last date possible, then switches to the forecast data.  Usually, we process the observed forcings up through the 15th to 25th of this initialization month, then the forecast forcing data carries the run forward for the remaining days in the month, and throughout the following 6 month forecast period.  Note, the state files used for the climatology runs correspond to the spin-up associated with the particular year (out of 1979-1999) from which the climatology ensemble member is drawn.
 the spin-up period captures the antecedent land surface hydrologic conditions for the forecast period: in the Columbia basin, the primary field of interest is snow water equivalent.
forecast products are spatial (distributed soil moisture, runoff, snowpack (swe), etc.), and spatial runoff + baseflow is routed to produce streamflow at specific points, the inflow nodes for a management model, perhaps. 

## Model forecasting domain

## East Coast hindcast

**East Coast hindcast**

## Approach:  1/8 - 1/4 degree implementation

## Pilot Forecasts:  Initial Conditions

- Jan 15, 2003

- Dec 28, 2002

- Feb 1, 2003

- Mar 1, 2003

- Apr 1, 2003

- This past winter,
- alarmingly low PNW December snowpacks mostly recovered by April, although some locations are still well off their long term averages

**Notes:**
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).

Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

## Winter 2002/03 forecasts: UW/NRCS comparison

- UW pilot results were comparable to the official streamflow forecasts of the National Resources Conservation Service (NRCS) streamflow forecast group (one location shown).

## UW West-wide forecast system – current domain and streamflow forecast points

**UW West-wide forecast system – current domain and streamflow forecast points**

- ~250 forecast points, including ~15 in Mexico
- Forecast models/methods include CPC “official” forecasts, ESP, and stratified ESP
- Forecasts for 6-12 month lead issued twice monthly (winter), monthly otherwise

## UW West-wide forecast system soil moisture nowcast (8/6/08)

- Daily updates, 24 hour lag effective ~2 pm Pacific
- Based on ~2000 index stations, adjusted to long-term (1915 – present) climatology

## Princeton University drought monitoring and prediction system

**Princeton University drought monitoring and prediction system**

- ~weekly nowcast update, eastern U.S. domain
- Uses NLDAS forcings
- Focus on (soil moisture) drought nowcast and forecast
- Forecasts based on Bayesian MME merging of GFS and ESP

## UW National Surface Water Monitor

**UW National Surface Water Monitor**
- 1⁄2 degree spatial resolution
- Updates daily (same lag as west-wide system)
- Same index station approach as west-wide system
- Climatology 1915-present

## UW Multi-model monitor

**UW Multi-model monitor**
- Same approach as VIC-based SWM
- Models include VIC, Noah, CLM, Sac

## Multi-model Ensemble

- For each model, re-express current soil moisture as percentile of climatology for this day of year

- Model *i*
- soil moisture

- Model *i*
- percentile

- Average all models’ percentiles
-  = 1/N Σ (*i*=1 to N) percentile *i*

- Multi-Model
- percentile

- Multi-model ensemble result is the percentile of the average of model percentiles

- This procedure occurs separately for each grid cell

## Soil Moisture Percentiles w.r.t. 1920-2003

- 2008-07-01

- CLM

- SAC

- NOAH

- ENSEMBLE

- VIC

- US Drought Monitor

## US Drought Monitor

- UW Surface Water Monitor
- Multimodel Ensemble

- Jul 1

- Aug 5

- Sep 2

- Agreement: WI drying trend

- Agreement: Gulf wetting trend

- Disagreement: Dry conditions in N.,S. Carolina?

- Agreement: Dry west coast

## Soil Moisture Percentiles w.r.t. 1916-2004

- 2008-07-01

- CLM

- SAC

- NOAH

- ENSEMBLE

- VIC

- US Drought Monitor

- Multimodel results with drought monitor color scheme (truncated at 30th percentile)

## US Drought Monitor

- UW multimodel SWM Summer 2008

- Jul 1

- Aug 5

- Sep 2

## Ongoing unification of UW and Princeton systems

- a)  unified nowcast (completed, in testing)
- b)  expansion of multimodel SWM domain into Mexico (in progress)
- c)  merger of forecast methods (esp. multimodel Bayesian MME) – planned
- d)  improved data assimilation – planned
- e)  multiple (land) model forecasts – planned
- f)  reservoir storage forecasts -- planned

## Conclusions and challenges

- Need for national scale hydrological prediction (including streamflow)
- Need for better ways of including a historical perspective (what historical period?) post-data assimilation
- Need for site-specific calibration (MOS-type approaches?) and verification
- Mechanisms for inclusion of local information?