Calibration and sensitivity analysis of SWAT for a small forested catchment, northcentral
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1 Calibration and sensitivity analysis of SWAT for a small forested catchment, northcentral Portugal Rial-Rivas, M.E. 1 ; Santos, J. 1 ;Bernard-Jannin L. 1 ; Boulet, A.K. 1 ; Coelho, C.O.A. 1 ; Ferreira, A.J.D. 4 ; Nunes, J.P. 1 ; Rodríguez-Suárez J.A. 1, 2 ; Rodríguez-Blanco M.L. 1,3 ; Keizer, J.J. 1 1 CESAM and Dept. Environment & Planning. University of Aveiro. PORTUGAL m.rial@ua.pt 2 Vegetal Biology and Soil Science Department. University of Vigo. SPAIN. 3 Faculty of Sciences. University of A Coruña. SPAIN. 4 CERNAS, Coimbra Agrarian Technical School. PORTUGAL 2011 International SWAT Conference- June Toledo, Spain UNIÃO EUROPEIA Fundo social Europeu
2 HIDRIA project UNIÃO EUROPEIA Fundo social Europeu
3 HIDRIA project A multi-stage approach for addressing input data uncertainties in process-based rainfall-runoff modeling for small forested catchments upstream of the Ria de Aveiro The project foresees the development of: A stepwise approach to rainfall-runoff modelling To assess the implications of existing data constraints To establish priorities for additional field and laboratory data gathering UNIÃO EUROPEIA Fundo social Europeu
4 SPECIFIC OBJECTIVE WITHIN THIS WORK Number of parameters included in the auto-calibration. HIDRIA project Assess the influence of: METHODS Ranges of variation of these parameters in the SWAT model auto-calibration results for the study catchment. SENSITIVITY ANALYSIS (ArcSWAT 2009 interface) AUTO-CALIBRATION (ArcSWAT 2009 interface) MODEL EVALUATION Latin Hypercube (LH) and One-factor-At-a-Time (OAT) sampling. Parameter Solution (ParaSol) with uncertainty analysis Nash-Sutcliffe efficiency (NSE) Percent bias (PBIAS) Root Mean Square Error (RMSE)
5 HIDRIA project STUDY AREA
6 HIDRIA project STUDY AREA Caramulo mountain range 4 micro-catchments Area <1 km 2 Serra de Cima Rainfall: mm/yr
7 HYDROLOGICAL MODELLING: Input Data
8 HYDROLOGICAL MODELLING: Input Data Data widely available in Portugal (e.g. to hydrologists from a consultancy company developing a Watershed Management Plan) ArcSWAT 2009 ELEVATIONS CLIMATE LAND-USE SOIL-TYPES HUMIC CAMBISOL
9 HYDROLOGICAL MODELLING: CLIMATE Input Data DAILY Rainfall Temperature Relative Humidity Wind velocity Solar radiation WEATHER GENERATOR Temperature Rainfall IM: Coimbra_G Study area
10 HYDROLOGICAL MODELLING: LAND-USE Input Data CORINE LAND-COVER 2006 CLC06: Broad-leaved forest: 93.42% total area Eucalypt Forest Catchment area: 0.53 km 2 CLC06: Transitional woodland-shrub 6.58% total area Young Eucalypt plantations
11 HYDROLOGICAL MODELLING: Simulated period
12 Rainfall, mm/yr Temperature, ºC HYDROLOGICAL MODELLING: Simulated period Warm-up period Study period Calibration period Mean Rainfall = 1245 mm/yr Validation period Rainfall, mm/yr Mean Temp, ºC
13 HYDROLOGICAL MODELLING: Simulated period Daily Streamflow, mm P Bouça = 1585 mm Qmm = 1004 mm Q max obs = 47.9 mm Q min obs = 0 mm Daily Rainfall, mm Daily Streamflow, mm P Bouça = 1367 mm Qmm = 671 mm Q max obs = 19.9 mm Q min obs = 0 mm Daily Rainfall, mm 0 1/1/09 3/2/09 5/1/09 6/30/09 8/29/09 10/28/09 12/27/ /01/10 03/02/10 05/01/10 06/30/10 08/29/10 10/28/10 12/27/ Calibration period Validation period
14 HYDROLOGICAL MODELLING: Sensitivity Analysis
15 Alpha_Bf Biomix Blai Canmx Ch_K2 Ch_N2 Cn2 Epco Esco Gw_Delay Gw_Revap Gwqmn Revapmn Sftmp Slope Slsubbsn Smfmn Smfmx Smtmp Sol_Alb Sol_Awc Sol_K Sol_Z Surlag Timp Tlaps Alpha_Bf Biomix Blai Canmx Ch_K2 Ch_N2 Cn2 Epco Esco Gw_Delay Gw_Revap Gwqmn Revapmn Sftmp Slope Slsubbsn Smfmn Smfmx Smtmp Sol_Alb Sol_Awc Sol_K Sol_Z Surlag Timp Tlaps HYDROLOGICAL MODELLING: Sensitivity Analysis INPUT OUTPUT 26 flow-related parameters Ranking PARAMETER LO BOUND UP BOUND imet Alpha_Bf Biomix With observed data Without observed data Blai Canmx Ch_K Ch_N Cn Epco Esco Gw_Delay Gw_Revap Gwqmn Revapmn Sftmp Slope Slsubbsn Smfmn With observed data Without observed data Mean Smfmx Smtmp Sol_Alb Sol_Awc Sol_K Sol_Z Surlag Timp Tlaps
16 Alpha_Bf Biomix Blai Canmx Ch_K2 Ch_N2 Cn2 Epco Esco Gw_Delay Gw_Revap Gwqmn Revapmn Sftmp Slope Slsubbsn Smfmn Smfmx Smtmp Sol_Alb Sol_Awc Sol_K Sol_Z Surlag Timp Tlaps Alpha_Bf Biomix Blai Canmx Ch_K2 Ch_N2 Cn2 Epco Esco Gw_Delay Gw_Revap Gwqmn Revapmn Sftmp Slope Slsubbsn Smfmn Smfmx Smtmp Sol_Alb Sol_Awc Sol_K Sol_Z Surlag Timp Tlaps HYDROLOGICAL MODELLING: Sensitivity Analysis 26 flow-related parameters WITHOUT WITH OBS DATA OBS DATA Alpha_Bf Esco Cn2 Gwqmn Esco Sol_Awc Canmx Canmx Blai Cn2 Surlag Sol_Z Ch_K2 Gw_Revap Gw_Revap Blai Gw_Delay Alpha_Bf Sol_Z Sol_K Ch_N2 Ch_K2 Sol_Awc Biomix Sol_K Slope Slope Epco Biomix Revapmn Slsubbsn Gw_Delay Epco Surlag Gwqmn Ch_N2 Sol_Alb Sol_Alb Revapmn Slsubbsn Sftmp Sftmp Smfmn Smfmn Smfmx Smfmx Smtmp Smtmp Timp Timp Tlaps Tlaps With observed data Without observed data With observed data Without observed data OUTPUT Ranking Mean
17 HYDROLOGICAL MODELLING: Auto-calibration
18 HYDROLOGICAL MODELLING: Auto-calibration Three auto-calibrations were carried out using: ParaSol with uncertainty analysis Fixing the number of simulations runs in and the optimization settings as the default. Auto-calibration A 26 flow-related parameters and full default ranges of variation Auto-calibration B 13 flow-related parameters and full default ranges of variation Auto-calibration C 13 flow related parameters and narrow ranges of variation The 13 most sensitive parameters selected from Sensitivity Analysis using observed data. Upper and lower bounds for narrow ranges using max and min values from the good parameter sets from Auto-calibration A.
19 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Calibration period
20 Daily Streamf low, mm Daily Rainf all, mm Daily Streamflow, 60 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Calibration period Satisfactory 30 Daily Rainfall, mm 60 SD= Daily Observed Flows, mm (Calibration period) Auto-calibration A: Daily Simulated Flows, mm (Calibration period) Auto-calibration A Auto-calibration B Auto-calibration C Auto-calibration B: Daily Simulated Flows, mm (Calibration period) PBIAS Auto-calibration 9.84 C: Daily Simulated Flows, mm (Calibration period) 9.75 RMSE NSE Very good /1/2009 2/1/2009 3/1/2009 4/1/2009 5/1/2009 6/1/2009 7/1/2009 8/1/2009 9/1/ /1/ /1/ /1/2009 1/1/2009 2/1/2009 3/1/
21 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Validation period
22 Daily Streamf low, mm Daily Rainf all, mm Daily Streamf low, mm 40 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Validation period Daily Rainfall, mm Daily Observed Flows, mm (Validation period) 20 Auto-calibration A: Daily Simulated Flows, mm (Validation period) Auto-calibration B: Daily Simulated Flows, mm (Validation period) Auto-calibration C: Daily Simulated Flows, mm (Validation period) SD=2.83 Auto-calibration A Auto-calibration B Auto-calibration C PBIAS RMSE NSE /01/10 01/31/10 03/02/10 04/01/10 05/01 01/01/10 01/31/10 03/02/10 04/01/10 05/01/10 05/31/10 06/30/10 07/30/10 08/29/10 09/28/10 10/28/10 11/27/10 12/27/
23 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Comparison between best parameter sets for each auto-calibration
24 HYDROLOGICAL MODELLING: Auto-calibration RESULTS Comparison between best parameter sets for each auto-calibration Auto-calibration A Auto-calibration B Auto-calibration C Alpha_Bf Biomix Blai Canmx Ch_K Cn Esco Gw_Revap Gwqmn Sol_Awc Sol_Awc Sol_K Sol_K Sol_Z Sol_Z Depth from soil surface to bottom layer Baseflow recesion coefficient is a direct index of groundwater flow response to changes in recharge Canopy Interception and Max. Potencial LAI Groundwater revap coefficient Afects the amount of water that recharges the capillary fringe after evaporation during the dry periods. Threshold depth of water in the shallow aquifer required for the return flow to occur (The ground water flow to the main channel is allowed only when the depth of water in the shallow aquifer is equal to or greater than
25 CONCLUSIONS The best results were obtained for the set with the largest number of parameters and the widest ranges of variation. Sensitivity analysis was helpful in reducing the number of parameters included in the auto-calibration and, auto-calibration time, without seriously affecting model results. The use of narrow ranges of variation for the parameters also reduced the time needed for auto-calibration whilst still producing results that can be regarded adequate, especially for general-purpose studies. The fact that several parameter sets have given good results, indicating a problem with equifinality of model parameterization. ONGOING WORK 1.- Check the feasibility of the obtained parameters 2.- Check other auto-calibration methodologies and with different objective functions. 3.- Testing SWAT with data obtained from a meteorological station in the study area as well as from fieldwork in the Serra de Cima catchment, aiming at improving model results and decrease problems related with equifinality.
26 Thanks for your attention UNIÃO EUROPEIA Fundo social Europeu
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