Streamflow Predictability Tom Hopson

Conduct Idealized Predictability Experiments Document relative importance of uncertainties in basin initial conditions and weather and climate forecasts on streamflow Account for how uncertainties depend on – type of forcing (e.g. precip vs. T) – forecast lead-time – Regions, spatial-, and temporal-scales Potential implications for: – how to focus research efforts (e.g. improvements in hydrologic models vs data assimilation techniques) – observational network resources (e.g. SNOTEL vs raingauge) – anticipate future needs (e.g. changes in weather forecast skill, impacts of climate change)

Initial efforts Start with SAC lumped model and SNOW-17 – (ignoring spatial variability) Applied to different regions – Four basins currently Drive with errors in: – initial soil moisture states (multiplicative) – SWE (multiplicative) – Observations (ppt – multiplicative; T – additive) – Forecasts with parameterized error growth Place in context of climatological distributions of variables to try and generalize regional and seasonal implications (e.g. forecast error in T less important in August compared to April in snow-dominated basins)

Historical Simulation Q SWE SM Historical Data PastFuture SNOW-17 / SAC Sources of Predictability 1.Run hydrologic model up to the start of the forecast period to estimate basin initial conditions; Model solutions to the streamflow forecasting problem…

Historical Simulation Q SWE SM Historical DataForecasts PastFuture SNOW-17 / SAC 1.Run hydrologic model up to the start of the forecast period to estimate basin initial conditions; 2.Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts. Sources of Predictability Model solutions to the streamflow forecasting problem…

Physically based conceptual model Two-layer model – Upper layer: surface and interception storages – Lower layer: deeper soil and ground water storages Routing: linear reservoir model Integrated with snow17 model Sacramento Soil Moisture Accounting (SAC-SMA) model Rainfall - Evapotranspiration - Changes in soil moisture storage = Runoff Model parameters: 16 calibrated parameters Input data: basin average precipitation (P) and Potential Evapotranspiration (PET) Output: Channel inflow (Q) Model parameters: 16 calibrated parameters Input data: basin average precipitation (P) and Potential Evapotranspiration (PET) Output: Channel inflow (Q)

Study site: Greens Bayou river basin in eastern Texas Drainage area: 178 km 2 Most of the basin is highly developed Humid subtropical climate mm annual rain Unit Hydrograph: Length 31hrs, time to conc 5hr Greens Bayou basin DJ Seo

Forcing and state errors Observed MAP – multiplicative – [0.5, 0.8, 1.0, 1.2, 1.5] Soil moisture states (up to forecast initialization time) – multiplicative – [0.5, 0.8, 1.0, 1.2, 1.5] Precipitation forecasts – error growth model

Forecast Error Growth models Lorenz, 1982 – Primarily IC error E small E large Another options: Displacement / model drift errors: E ~ sqrt(t) (Orrell et al 2001)

Error growth, but with relaxation to climatology Probability/m Precipitation [m] Error growth around climatological mean Probability/m Precipitation [m] Error growth of extremes Short-lead forecast Longer-lead forecast Climatological PDF => Use simple model Where: p f (t) = the forecast prec err static = fixed multiplicative error w(t) = error growth curve weight p o (t) = observed precip q c = some climatological quantile Err static = [0.5, 0.8, 1.0, 1.2, 1.5] q c = [.1,.25,.5,.75,.95] percentiles

Greens Bayou Precip forcing fields – Nov 17, 2003 tornado Perturbed obs ppt [mm/hr] Perturbed fcst ppt All perturbed (including soil moisture) Q response [mm/hr] Perturbed soil moisture (up to initialization) Note: high ppt with low sm (aqua) Low ppt with high sm (green)