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)

Flood of record Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation Snoqualmie River at Carnation, WA

How important is calibration for seasonal hydrologic prediction?

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

From Wood and Lettenmaier (BAMS, 2006): 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 3 rd column ~= 1 st column)

Downscaling Test 1.Start with GSM-scale monthly observed met data for 21 years 2.Downscale into a daily VIC-scale time series 3.Force hydrology model to produce streamflow 4.Is observed streamflow reproduced?

Simulations Forecast Products streamflow soil moisture runoff snowpack VIC model spin-up VIC forecast ensemble climate forecast information (from GSM) VIC climatology ensemble 1-2 years back start of month 0end of month 6 NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up data sources A B C

Model forecasting domain

East Coast hindcast

Approach: 1/8 - 1/4 degree implementation Pilot scale implementation Pacific Northwest Updates Dec 28, 2002ESP Jan 15, 2003ESP Feb 1ESP, GSM, NSIPP Feb 15ESP Mar 1ESP, GSM, NSIPP Mar 16ESP Apr 1ESP, GSM, NSIPP

Pilot Forecasts: Initial Conditions Jan 15, 2003Dec 28, 2002 Feb 1, 2003 Mar 1, 2003Apr 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

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 ~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 ~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 ½ degree spatial resolution Updates daily (same lag as west-wide system) Same index station approach as west- wide system Climatology present

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

0 100 Multi-Model Cumulative Probability, Soil Moisture (mm) % Multi-model Ensemble Model i Cumulative Probability, Soil Moisture (mm) % 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 CLM SACNOAH ENSEMBLE VIC 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 CLM SACNOAH ENSEMBLE VIC US Drought Monitor Multimodel results with drought monitor color scheme (truncated at 30 th percentile)

US Drought MonitorUW 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?