Hydrology and Water Management Applications of GCIP Research Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington for presentation at Stakeholder Session Mississippi River Climate and Hydrology Conference New Orleans May 14, 2002

GEWEX and GCIP Goals Scientific goal: “To observe and model the hydrologic cycle and energy fluxes in the atmosphere, and at the land and ocean surface” Applications goal: “Demonstrate skill in predicting changes in water resources on time scales up to seasonal, annual and interannual”

GCIP Contributions for Water Management Data sets Macroscale land surface model development and improvements Land Data Assimilation System (LDAS) Forecast method and product development

Data Sets Many, key examples include: WSR-88d retrospective archive (1996-2000) GOES solar radiation LDAS retrospective (model forcing, e.g., precipitation, and model-derived, e.g., soil moisture) LDAS real time

LDAS Modeling Domain Domain is North America between 25º and 53º N Resolution 1/8º 77,000 grid cells through domain (56,000 in Continental U.S.) Model developed for 15 sub-regions Model forcing data (1949-2000) derived from observations Run at a 3-hour time step

LDAS Derived Soil Moisture - Active Range Long term spatial data set allows characterization of variability Dynamic range of the soil column Degree to which source of variability (P) is buffered by soil column Level of hydrologic interaction of soil column 50-Year Soil Moisture Range Scaled by Annual Precipitation

Macroscale land surface model development and improvements

VIC Hydrologic Model

Land Data Assimilation System (LDAS)

LDAS land surface model (LSM) and coupled model interaction

LDAS real-time soil moisture

Forecast method and product development

Coupled GCM Embedded Regional CM Macro-Scale Hydrology Model (VIC) SST Forecast

How NCEP makes experimental ensemble climate forecasts Coupled Ocean-AGCM 20 different Initial states of the atmosphere 20 unique forecasts of the climate over the next six months 2x2 degree AGCM Initial and Predicted SST for next 6 months We use the raw model output at this point. …but, we must recognize the scale mismatch

GSM PCP OBS PCP PROB PROB RAW FORECAST PCP BIAS-CORR PCP

East Coast Apr ’00 forecast for May-Jun-Jul forecast median shown as percentile of climatology ensemble

CRB May forecast forecast hindcast “observed” forecast medians

CRB May Forecast cumulative flow averages important point(s): plotted against the 1961-1990 streamflow climatology used by other agencies, the forecast ensemble medians for average flow in the first 3 months of the forecast period (top) and over all six months (bottom) end up between the 1977 and 1988 runoff averages. Given the uncertainties in the snow pack estimation used as initial conditions for the forecast runs, particularly the fact that our estimation is somewhat less dire than that of the NRCS (putting initial snowpack below their estimates for 1977), these results could be looked at as conservative – that is, 2001 runoff could actually be lower than we are showing here. forecast medians

Information Xfer to Stakeholders: How well did GCIP succeed in “demonstrating skill”? Conclusion: Quite well, if the goal is taken literally but Demonstrated use of GCIP “products” by water managers is minimal why?

Scientists are not particularly skilled, or even interested in, “technology transfer” Practitioners are stuck in “paradigm lock”; institutional inertia on algorithms and methods GCIP/GAPP has provided science, not applications funding -- focus on many small projects of limited duration discourages long-term view Possible solution: better liaison with RISAs as mechanism for breaking “paradigm lock”