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

Past Future

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 Data Forecasts

Past Future

SNOW-17 / SAC 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 PredictabilityModel 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)

Study site: Greens Bayou river basin in eastern Texas

• Drainage area: 178 km2

• Most of the basin is highlydeveloped

• Humid subtropical climate890-1300 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 climatologyPr

obab

ility

/m

Precipitation [m]

Error growth around climatological mean

Prob

abili

ty/m

Precipitation [m]

Error growth of extremes

Short-lead forecast

Longer-lead forecast

Climatological PDF

=> Use simple model

Where: pf(t) = the forecast precerrstatic = fixed multiplicative errorw(t) = error growth curve weightpo(t) = observed precipqc = some climatological quantile

Errstatic = [0.5, 0.8, 1.0, 1.2, 1.5]

qc = [.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)