Development of Urban Modeling Tools in SWAT

Transcription

1 29 International SWAT conference August 5-7 Boulder, CO Development of Urban Modeling Tools in SWAT J. Jeong, N. Kannan, J. G. Arnold, R. Glick, L. Gosselink, R. Srinivasan LOGO

2 Tasks in urban SWAT project Sub-hourly flow Sub-hourly erosion and sediment yield Urban SWAT Stormwater BMPs

3 Contents Project Overview Methods & Study Area Sensitivity Analysis Calibration & Validation FDC Analysis Summary Progress in Erosion models

4 Methods SWAT code modified for sub-hourly simulation Surface runoff Green & Ampt method Channel flow Variable storage method, Muskingum method Impoundments ponds, reservoirs Base flow and ET simulated daily Complexity in SWAT code Basis of the base flow models used in SWAT As alternative, base flow is estimated at daily interval, then evenly distributed to subdaily time steps to compute total inflow to the channel Fixed critical errors in SWAT25 Unit hydrograph Procedures on continuous simulation over midnight

5 Methods Subdaily surface runoff lag New surface runoff lag equation is used for subdaily estimation Q surf, i ' ( ) surlag Q surf, i + Qstor, i 1 1 exp tc / t = fraction of surface runoff storage reaching sstream surlag=1 surlag=5 surlag= fraction of surface runoff storage reaching stream surlag=1 surlag=5 surlag= t c, hours t c / t (Current ) (New )

6 Methods Triangular Unit hydrograph Route overland flow in a subbasin Pulse input excess rainfall is distributed in a triangular shape t p estimated by SCS dimensionless method All UHs are superimposed at computational nodes q uh = t / t t p t if t t b q uh = if t > tb t p t p p t b = t + tb _ adj c t =. 375 p t b

7 Study area Study area Area: 1.94 km 2 7% undeveloped, pasture/gc (18%), residential (12%) Average slope: 6.2% Stream flow: 45% base flow, 55% surface runoff, highly ephemeral LGA Watershed in Austin, TX m m GIS input and historical data.3m (1ft) DEM Land cover surveyed in 23 Soil map: SSURGO Delineation: 4 subbasins and 36 HRUs Weather data: subdaily precip, daily Temp Stream data: continuous 15min flow record

8 SWAT Settings Subdaily interval (15min &1hr) Daily interval Surface runoff Green & Amp Curve Number Overland flow routing Surface runoff lag + unit hydrograph Surface runoff lag Channel routing Muskingum Muskingum Precip interval 15min/1hr (= t) Daily Temperature Daily max/min Daily max/min ET Hargreaves Hargreaves

9 Sensitivity analysis Parameter Definition File name Range of values Min. Max. ALPHA_BF Base flow recession constant (days).gw.1 1 SURLAG Surface runoff lag coefficient (days).bsn.1 15 AWC Available water capacity.sol -25% * +25% CH_K 1,2 Effective hydraulic conductivity of.rte channel (mm/hr).sub CH_N 1,2 Manning's n value for the main and.rte tributary channels.sub.1.7 CN2 SCS runoff curve number for moisture condition II.mgt -4. ** +4. EPCO Plant uptake compensation factor.hru.1 1 ESCO Soil evaporation compensation factor.hru.1 1 GW_DELAY Delay time for aquifer recharge (days).gw.1 1 GW_REVAP Groundwater revap coefficient.gw.2.2 GWQMN Threshold water level in shallow aquifer for base flow (mm).gw.1 1 Ksat Saturated hydraulic conductivity (mm/hr).sol -5% +5% MUSK_CO1 Weighting factor for influence of normal flow on storage time constant.bsn.1 1 value MUSK_CO2 Weighting factor for influence of low flow on storage time constant.bsn.1 1 OVR_N Manning's n value for overland flow.hru.5.8 Latin hypercube sampling with One factor At a Time (LH-OAT) A global method that evaluates relative sensitivity between parameters LH-OAT engine downloaded from SAHRA Hydroarchive 15 SWAT parameters related to flow were evaluated with 15min, 1hr, and 1day interval Question: Does temporal resolution have an impact on sensitivity? * Value varies with land use; changes by multiplying a ratio within the range ** Value varies with land use; changes by adding/subtracting a value within the range

10 Sensitivity analysis - Result 15 minute 1 hour 1 day Rank Parameter S i Parameter S i Parameter S i 1 CH_N ALPHA_BF 25.7 AWC AWC 43.4 AWC 24.5 GWREVAP SURLAG 19.5 GWQMN 9.5 GWQMN MSK_CO2 9.2 ESCO 8.6 ESCO KSAT 8.8 GWDELAY 7. ALPHA_BF OVR_N 8.1 CN2 4.7 GWDELAY ALPHA_BF 6.8 GWREVAP 3.4 CN ESCO 6.6 KSAT 2.4 SURLAG CH_K 5.7 SURLAG 1.1 KSAT CN2 4.9 CH_N.7 EPCO.5 11 GWDELAY 4.8 MSK_CO2.4 OVR_N.2 12 MSK_CO1 2.9 CH_K.4 MSK_CO GWQMN 1.2 EPCO.3 CH_N.1 14 GWREVAP.9 OVR_N.2 MSK_CO EPCO.6 MSK_CO1.1 CH_K. Yes, parameter sensitivity is affected by operational time interval Channel flow parameters get more sensitive as time interval decreases from 1day to 15min Groundwater flow parameters get more sensitive as time interval increases from 15min to 1day AWC is highly sensitive irrespective of time step KSAT, ESCO, CN2, AND SURLAG are marginally ranked

11 Calibration Strategy 3 scenarios calibrated independently 15min, 1hour, and 1day interval Different parameters calibrated for different scenarios 5 highly ranked parameters in SA Semi-automatic procedure (automatic + manual) Manual calibration Base flow ratio monitored Narrow down parameters ranges for autocalibration Auto calibration Simulate different combination of parameters Collect calibration statistics Statistical index: Nash & Sutcliffe Efficiency (NSE) and R 2 Simulation period : 5 years (2 24) Calibration: 1 year (24) with two years of warm up period (22-23) Validation: 1 year (22) with two years of warm up period (2-21)

12 Calibration Periods Criteria: annual rainfall & land use change Calibration 24 Validation ,187mm 924mm 954mm 875mm mm 482mm

13 Calibration Daily stream flow (NSE=.72, R 2 =.74) 6 5 Stream Flow, mm Rainfall, mm Days in Year 24 Rainfall Observed flow Predicted flow

14 Calibration cont. 15min stream flow showing daily total (NSE=.93) 6 5 Stream Flow, mm Rainfall, mm Days in Year 24 Rainfall Observed flow Predicted flow

15 Calibration cont. 15min stream flow showing 15min interval (NSE=.74, R 2 =.76) Stream Flow, mm Rainfall, mm Days in November 24 Rainfall Observed flow Predicted flow

16 Validation Daily simulation Hourly simulation 5 5 Stream Flow, mm Rainfall, mm Stream Flow, mm Rainfall, mm Days in 22 Days in 22 Rainfall Observed flow Predicted flow Rainfall Observed flow Predicted flow NSE 1day =.65 NSE 1hr-1day =.9 (NSE 1hr =.72) NSE 15min-1day =.87 (NSE 15min =.63)

17 Flow Duration Curves Stream flow, mm Calibration - High flow Stream flow, mm Calibration - Mid to Low flow % Exceedance 1 1 % Exceedance Stream flow, mm 4 3 Validation High flow % Exceedance Stream flow, mm Validation Mid to Low flow % Exceedance

18 Summary Sub-hourly SWAT model is developed for rainfall-runoff simulation Model parameters related to channel flow get more sensitive at higher (subdaily) temporal resolution Model parameters related to groundwater flow get more sensitive at lower (daily) temporal resolution For the test watershed, sub-daily/sub-hourly simulations show significant improvement over daily results High flows are well predicted, but no improvement in low flows The improvement in predicting high flows will benefit NPS water quality modeling

19 Erosion and sediment yield modeling Splash erosion model Upland erosion process by impact of raindrops to soil Adapted from EUROSEM model Erosion is estimated based on the kinetic energy delivered by raindrops Overland flow erosion model Rill/interill erosion processes by overland flow MUSLE equation in SWAT25 is not adequate for sub-hourly simulation Adapted from ANSERS model Channel flow erosion model Bagnolds equation (SWAT25) Yang s models for sand and gravel Brownlie s model

20 Preliminary results Riesel watershed (46.2ha) 2-24 Sediment Yield(15min load) Sediment Yield (15min load).5.6 sediment, tons/ha sediment, tons/ha time step (dt=15min), day 35 in 21 time step (dt=15min), day 62 in 23 Pred-SED tons/ha Obs-SED tons/ha Pred-SED tons/ha Obs-SED tons/ha

21 Future work BMP models Flow model Reconstruct SWAT routines for anytime interval simulation Green-Ampt equation for infiltration Triangular Unit hydrograph method Overland flow and stream flow routed at any time interval Erosion models Splash erosion model adapted from EUROSEM Overland flow erosion adapted from ANSWERS model Yang s model and Brownlie model for instream erosion Test and validation Fortran routines will be developed for physically based simulation of these BMPs Detention ponds Wet ponds Sedimentationfiltration Retention irrigation

22 LOGO