Towards a global drought prediction capability Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Workshop on Adapting to the impacts of global changes on river basins and aquifer systems UNESCO/IHP, Paris September 8, 2008

Outline Some characteristics of droughts A model-based approach to real-time drought estimation Drought forecasting and predictability U.S. multimodel implementation Challenges in global adaptation Summary

Characteristics of droughts Standard definition lacking, but meteorological drought mostly associated with precipitation deficits, agricultural drought with soil moisture, and hydrological drought with runoff Spatial as well as temporal dimension is critical Droughts don’t necessarily conform to river basin boundaries Slowly evolving, and mostly slowly ending Hydrologic predictability mostly due to ICs (snow and soil moisture storage)

Drought nowcasting– the U.S. experience U.S. DM is widely used, but link to direct observations (e.g., of soil moisture) is weak – hence reliance on indirect methods, such as PDSI. Need for reproducible basis for identifying drought-affected regions. Land surface model representations of soil moisture (and runoff) offer an alternative means for estimating severity, frequency, duration, and variability of current droughts, and linking them to the climatology of observed droughts.

Rational for model-based approaches Gridded observation-based precipitation products provide a basis for estimation of products like SPI No such equivalent soil moisture data exist over large areas While streamflow gauge networks exist (especially in developed countries), use of gauge data introduces spatial scale dependence, and management effects – difficult to extract information about spatial dimension of runoff from gauge observations

Extent of drought over continental U. S Extent of drought over continental U.S. based on reconstructed total column soil moisture, June 1988

Severity Area Duration analysis Andreadis et al, JHM, 2005) Similar to Depth Area Duration analysis (design storms) Encompasses drought characteristics (severity, spatial extent and duration) Each event has its own set of SAD curves

Soil Moisture SAD envelope curves 1930s and 1950s droughts most severe for short and long durations respectively 2000s W US drought among the most severe

Near-real time CONUS soil moisture based on VIC

Data Daily precipitation and temperature max-min, other land surface variables (downward solar and longwave radiation, near-surface humidity, and wind) derived via index methods. Methods as described in Maurer et al. (2002). Data duration is from 1915-2003, and period of analysis is 1920-2003 . Spatial resolution 0.5  (3322 land grid cells), domain conterminous United States. Soil and vegetation parameters are from different sources for different models (generally NLDAS), as provided by model developers. Other parameters are model standard setup.

Multi-model approach (for details see Wang et al, J Clim in review) VIC: Variable Infiltration Capacity Model (Liang et al. 1994) CLM3.5: Community Land Model version 3.5 (Oleson et al. 2007) NOAH LSM: NCEP, OSU, Air Force, Hydrol. research lab (Mitchell et al. 1994, Chen and Mitchell 1996) Sacramento (grid-based version of NWS operational hydrologic prediction system; uses NOAH PET) (Burnash et al, 1973)

The challenge: Different land schemes have different soil moisture dynamics Model simulated soil moisture at cell (40.25N, 112.25W)

Solution: Normalize total column soil moisture as percentiles relative to each model’s climatology For each model, total column soil moisture was expressed as percentiles (hence by construct, uniformly distributed from zero to one). Percentiles were estimated for each model by month, using simulated total column soil moisture for the period 1920-2003. Percentiles were computed using the Weibull plotting position formula.

Areas for spatially averaged soil moisture percentiles NW NE SW SE Box sizes are 5 x 5 degrees

NW

NE

SW

SE

Multimodel results from UW real-time surface water monitor, 11/07 – 1/08

UW SWM for 9/7/08 (updated 9/5/08)

Drought forecasting – ensemble forecast approaches

DEMETER forecast predictability evaluation VIC model long-term (1960-99) simulations at ½ degree spatial resolution assumed to be truth DEMETER reforecasts with ECMWF seasonal forecast model for 6 month lead, forecasts made on Feb 1, May 1, Aug 1, Nov 1 1960-99 9 forecast ensembles on each date Forecast forcings (precipitation and temperature) downscaled and bias corrected using Wood et al approach (also incorporated in UW West-wide system) On each forecast date, 9 ensemble members also resampled at random from 1960-99 to form ESP ensemble Forecast skill evaluated using Cp for unrouted runoff

Test sites

Missouri River at Fort Benton

Snake River at Milne

Owyhee River

Riley

Rosco

Challenges in global implementation Availability of accurate real-time precipitation (satellite products are only viable alternative over much of the globe) Reliable historical climatology for evaluation of drought statistics (transformation to percentiles Validation data

TRMM multi-satellite precipitation “research product”, evaluation over La Plata basin

Bias in TRMM TMPA research and real-time precipitation products in La Plata basin, 2003-2005

Implementation in African Drought Monitor (Sheffield et al, GEWEX NEWS, Aug. 2008)

Summary and Conclusions Current drought products suffer from the absence of reproducible, objective methods for identifying drought extent and severity. Land surface parameterizations, such as the family of NLDAS models, avoid these shortcomings. However, soil moisture, a key drought-related variable, is model-dependent Multi-model estimates of soil moisture, appropriately normalized, address all of the above shortcomings. When forced with common observations, major drought events appear to be plausibly reproduced by the individual models, and two methods of combining results into a multi-model ensemble. The challenges in global implementation of these methods have to do mostly with data availability and quality. Particularly for data-sparse areas (where improved drought nowcasting and forecasting is arguably most needed) improvements in consistency between retrospective and real-time precipitation products (effectively reanalysis of satellite products) are needed.