The Use of River Flow Discharge and Sediment Load for Multi-Objective Calibration of SWAT Based on the Bayesian Inference
Abstract
:1. Introduction
2. Study Area
2.1. Baocun Watershed
2.2. Model Input Data
3. Methods
3.1. SWAT-WB-VSA Model
3.1.1. Surface Runoff Generation
3.1.2. Sediment
3.2. Model Calibration
3.3. Transformation of Flow and Sediment Objectives to a Likelihood Function
3.3.1. Case with NSE
3.3.2. Case with BC-GED
4. Results
4.1. NSE Approach
4.2. BC-GED Approach
5. Discussion
5.1. Effects of Multi-Objective Approach
5.2. Difference between NSE and BC-GED Error Model
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Soil Types | Soil Particle Distribution (%) a | Organic Carbon (% Weight) | Conductivity (10−6 m/s) | USLE_K b | |||
---|---|---|---|---|---|---|---|
Gravel | Sand | Silt | Clay | ||||
Regosols | 20 | 38 | 27 | 15 | 0.98 | 7 | 0.174 |
Luvisols | 4 | 39 | 36 | 21 | 0.74 | 6 | 0.173 |
Fluvisols | 9 | 72 | 14 | 5 | 0.41 | 33 | 0.143 |
Categories | Parameter | Range | Alter Type a | Definition | |
---|---|---|---|---|---|
Min | Max | ||||
Evapotranspiration | ESCO | 0.01 | 1 | v__ | Soil evaporation compensation factor |
EPCO | 0.01 | 1 | v__ | Plant uptake compensation factor | |
Surface water | EDC | 0 | 1 | v__ | Effective depth of the soil profile |
OV_N | 0.005 | 0.5 | v__ | Manning’s “n” value for overland flow | |
SURLAG | 0 | 24 | v__ | Surface runoff lag coefficient | |
Soil water | SOL_Z | 10% | 3 | r__ | Soil thickness |
SOL_BD | 40% | 2 | r__ | Moist bulk density | |
SOL_AWC | 1% | 4 | r__ | Available water capacity of the soil layer | |
SOL_K | 1% | 11 | r__ | Saturated hydraulic conductivity | |
Ground water | GW_DELAY | 0 | 60 | v__ | Groundwater delay time (days) |
ALPHA_BF | 0 | 1 | v__ | Baseflow recession constant | |
GWQMN | 0 | 1000 | v__ | Threshold depth of water in the shallow aquifer required for return flow to occur (mm) | |
RCHRG_DP | 0 | 1 | v__ | Deep aquifer percolation fraction | |
REVAPMN | 0 | 1000 | v__ | Threshold depth of water in the shallow aquifer for revaporization (mm) | |
GW_REVAP | 0.02 | 0.2 | v__ | Groundwater revaporization coefficient | |
Tributary/main channel | CH_N1 | 0.005 | 0.15 | v__ | Manning’s “n” value for the tributary channels |
CH_N2 | 0.005 | 0.15 | v__ | Manning’s “n” value for the main channels | |
MUSLE | USLE_K1 | 0 | 1 | v__ | Regosols erodibility factor (Uphill) |
USLE_K2 | 0 | 1 | v__ | Luvisols erodibility factor (Sidehill) | |
USLE_K3 | 0 | 1 | v__ | Fluvisols erodibility factor (Foothill) | |
ADJ_PKR | 0 | 10 | v__ | Subbasin peak rate adjustment factor | |
Sediment transport | PRF | 0 | 10 | v__ | Main channel peak rate adjustment factor |
SPCON | 0.0001 | 0.1 | v__ | Linear coefficient in sediment transport | |
SPEXP | 0.0001 | 6 | v__ | Exponent coefficient in sediment transport | |
CH_EROD | 0 | 1 | v__ | Channel erodibility factor |
Categories | Methods | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | Total |
---|---|---|---|---|---|---|---|---|---|
Flow (×86,400 m3) | Observation | 105.7 | 201.2 | 133.6 | 142.2 | 258.8 | 376.7 | 0.2 | 1218.4 |
Simulation (NSE) | 23.5 | 203.7 | 127.7 | 147.3 | 275.4 | 292.1 | 10.2 | 1079.9 | |
Simulation (BC-GED) | 91.9 | 213.5 | 117.0 | 135.9 | 198.6 | 255.6 | 20.8 | 1033.4 | |
Sediment (Ton) | Observation | 1634.3 | 8988.7 | 1081.4 | 5205.8 | 54,928.7 | 25,444.9 | 0.0 | 97,283.8 |
Simulation (NSE) | 251.8 | 10,138.0 | 3659.9 | 4784.6 | 57,388.2 | 20,788.1 | 302.4 | 97,313.0 | |
Simulation (BC-GED) | 513.2 | 9312.0 | 718.1 | 2606.3 | 56,632.3 | 15,452.1 | 2.4 | 85,236.4 |
Categories | NSE Approach | BC-GED Approach | |||
---|---|---|---|---|---|
Flow | Flow + Sed | Flow | Flow + Sed | ||
Flow (mm) | Evaporation | 489.50 | 492.90 | 520.60 | 521.20 |
Surface flow | 37.04 | 37.10 | 65.09 | 63.17 | |
Lateral flow | 12.49 | 13.17 | 90.82 | 90.39 | |
Ground flow | 199.84 | 197.43 | 102.47 | 107.18 | |
Revaporization | 76.73 | 74.31 | 0.00 | 12.94 | |
Deep percolation | 3.34 | 3.55 | 38.82 | 25.73 | |
Sediment a (Ton) | Total slope erosion | 18,101.2 | 25,185.9 | ||
Total river erosion | 10,246.1 | −4445.9 | |||
Level_2 river_5 erosion | 2863.1 | −1567.7 | |||
Level_2 river_6 erosion | 1925.5 | −1276.1 | |||
Level_3 river_7 erosion | 5457.5 | −1602.0 |
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Cheng, Q.-B.; Chen, X.; Wang, J.; Zhang, Z.-C.; Zhang, R.-R.; Xie, Y.-Y.; Reinhardt-Imjela, C.; Schulte, A. The Use of River Flow Discharge and Sediment Load for Multi-Objective Calibration of SWAT Based on the Bayesian Inference. Water 2018, 10, 1662. https://doi.org/10.3390/w10111662
Cheng Q-B, Chen X, Wang J, Zhang Z-C, Zhang R-R, Xie Y-Y, Reinhardt-Imjela C, Schulte A. The Use of River Flow Discharge and Sediment Load for Multi-Objective Calibration of SWAT Based on the Bayesian Inference. Water. 2018; 10(11):1662. https://doi.org/10.3390/w10111662
Chicago/Turabian StyleCheng, Qin-Bo, Xi Chen, Jiao Wang, Zhi-Cai Zhang, Run-Run Zhang, Yong-Yu Xie, Christian Reinhardt-Imjela, and Achim Schulte. 2018. "The Use of River Flow Discharge and Sediment Load for Multi-Objective Calibration of SWAT Based on the Bayesian Inference" Water 10, no. 11: 1662. https://doi.org/10.3390/w10111662
APA StyleCheng, Q. -B., Chen, X., Wang, J., Zhang, Z. -C., Zhang, R. -R., Xie, Y. -Y., Reinhardt-Imjela, C., & Schulte, A. (2018). The Use of River Flow Discharge and Sediment Load for Multi-Objective Calibration of SWAT Based on the Bayesian Inference. Water, 10(11), 1662. https://doi.org/10.3390/w10111662