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Streamflow Simulation Using a Semi-Distributed Version of SAC-SMA Model

Behnaz Khakbaz 1 , Newsha K. Ajami 2 ,Kuolin Hsu 1 and Soroosh Sorooshian 1 1 Center for Hydrometeorology and Remote Sensing (CHRS),UCIrvine 2 Berkeley Water Center, University of California,Berkeley. Streamflow Simulation Using a Semi-Distributed Version of SAC-SMA Model.

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Streamflow Simulation Using a Semi-Distributed Version of SAC-SMA Model

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  1. Behnaz Khakbaz1, Newsha K. Ajami 2 ,Kuolin Hsu1 and Soroosh Sorooshian1 1Center for Hydrometeorology and Remote Sensing (CHRS),UCIrvine 2 Berkeley Water Center, University of California,Berkeley Streamflow Simulation Using a Semi-Distributed Version of SAC-SMA Model

  2. Study River Basin : Illinois River Basin South of Siloam Spring, AR • Basin Area : 1489 km2 General View • Model: A Semi-Distributed Version of SAC-SMA • Data used in the study: • 30m resolution DEM • Basin boundary • Hourly streamflow data (USGS) • Vegetation • Inputs to the model: • Multisensor (NEXRAD +Gauge) precipitation data • Mean Free Water Surface Evaporation Data • Interested in: • Calibrated and uncalibrated simulations at • -The outlet of the basin • -The interior points

  3. Spatial and Temporal Resolution The criteria for sub-basin delineation: considering the confluents the specified interior points 15 Sub-basins Area of Sub-basins : 5 – 200km2 Average area : 100km2 Temporal Resolution: 1hr

  4. Extracting MAP from Multi-Sensor(NEXRAD+Gauge) Precipitation Data

  5. Hydrologic Model SACramento Soil Moisture Accounting model (SAC-SMA) • Used in NWS • Deterministic • Lumped • Non-linear

  6. Our Model Structure Overland flow Routing Sub-basin Outlet Bypass overland flow routing KW Routing along channel n = 0.025,Wb=35m (Ajami et al,2004)

  7. Lc L Derivation of UH for Sub-basins Empirical formulas tl = Ct ( L*Lc)0.3 tp = D/2 + tl Qp = 0.208 * A / tp Ct =0.6/S0.5 tl: lag time(hr) D:effective rainfall duration(hr) A: basin area(km2) tp:time to peak(hr) Qp:peak discharge(cms) Outlet

  8. Sub-basin Outlet KW Routing along the channel Model Parameters Parameters to be calibrated No parameter from overland flow and channel routing to be calibrated

  9. f fi ,i=1,..,15 f1=...=f15 fi ,i=1,..,15 f1=...=f15 Calibration Scenarios Semi-Lumped (SL): Distributed Input Lumped Parameters Semi-Distributed (SD): Distributed Input Distributed Parameters Lumped (L): Lumped Input Lumped Parameters

  10. Semi-distributed Calibration Scenario Using SAC-SMA parameter grids (Koren et al, 2000) for 11 parameters as apriori information in semi-distributed calibration scenario Pij=APij /APbj*Pj Pij: Adjusted Parameter j for sub-basin i APij: Averaged a priori parameter j for sub-basin i APbj: Averaged a priori parameter j for whole basin Pj : Common parameter j for all the sub-basins i=1,2,…,15 sub-basin j=1,2,…,11 parameter only 13 parameters(as opposed to 13*15=195) to be calibrated Similar to Vieux et al (2004) and Frances et al(2007)

  11. Places strong weights on low flow parts:Having good estimate of LZ params Places strong weights on high flow parts:Having good estimate of UZ params Calibrating LZ params To re-adjust them Calibration Tool and Method Calibration Tool:Shuffled Complex Evolution (SCE-UA)(Duan et al,1992) Multi-Step Automatic Calibration Scheme (Hogue et al , 2000)

  12. Sandy Loam Silty Loam Loam Uncalibrated:A Priori Parameters Zup=5.08(1000/CN-10)/(Ts-Tfld)

  13. Statistical Results of Simulations for the Outlet in Calibration Period

  14. Conclusion Uncalibrated simulations using a priori SAC-SMA parameters (Koren et al,2000) in the semi-distributed SAC-SMA are reasonably good. Calibration of the model can improve the simulation results comparing to the uncalibrated simulations. • Comparison of different calibration scenarios tried in our model shows • the best performance for Semi-Lumped parameter scenario for the outlet .This • result is somehow expected for a relatively homogeneous basin like Illinois • river basin at Siloam Spring. • Semi-distributed version of SAC-SMA has the potential toimprove the streamflow simulations at the outlet and meanwhile provide streamflow simulations for specified interior points .

  15. On-going Study • Potential Evapotranspiration: PE adjustmentfactors • using Global Vegetation Fraction (GVF) • Roughness Coefficients • Channel Cross Sections

  16. 4-90.4% 8-3.3% 12-2.2% 13-2.1% 14-2% Vegetation

  17. PET PEadj factors: Using Multi-year averaged Global Vegetation Fraction (GVF) for 52 weeks of a year and the relationship between GVF and PEadj factors(Koren et al 1998,unpublished report) GVF dataset derived from 25 years AVHRR data and extracted from the NOAA/NESDIS website

  18. Roughness Coefficients There were no cross-sections and roughness coef available for Illinois River basin above Siloam Spring. Empirical equation derived by Tokar and Johnson 1995, cited by Koren et al, 2003: nc= no Sc0.272 F-0.00011 F: upstream drainage area , Sc: slope of each channel section no estimated from the measurements at the basin outlet : USGS flow measurements at the basin outlet

  19. Channel Cross Sections Leopold & Madock (1953) cited in Frances et al(2007): Wb=a1 Qba1 Wb=a1 (k Af)a1 Qb= k Af Wb=Wo Af.a1 Wb : top width , Qb:bankfull discharge , A: drainage area to a particular section Frances et al(2007): f=0.75, a1=0.5 Assuming rectangular cross-sections and using the basin outlet data to estimate Wo

  20. Thank You

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