# Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Overview of SWAT

^{2}); and ${W}_{btm,fld}$ is the bottom width of the floodplain (m). Moreover, SWAT assumes that the slopes of the main channel and floodplain have a 2:1 and 4:1 run to the rise, respectively. Based on these assumptions, SWAT computes the bottom width of the main channel using the bankfull width and depth using Equation (4).

^{2}), ${P}_{ch}$ is the wetted perimeter of flow (m), ${v}_{c}$ is the flow velocity (m/s), $sl{p}_{ch}$ is the slope along the channel length (m/m), and n is Manning’s coefficient for the channel.

#### 2.2. Development of New Regression Equations for Estimating Channel Geometries

#### 2.2.1. Study Area

^{2}, which corresponds to a middle-scale watershed according to the standard of watershed classification by size [24]. The recent 10-year average temperature is 10.25 °C, and average annual precipitation is 1258 mm. Most of the watershed consists of forest areas, showing that the well-formed, narrow river valley is naturally distributed in both the main channels and tributaries from upstream to downstream.

#### 2.2.2. Development of New Regression Equations to Reflect Real Channel Geometry Information

#### 2.3. Modification of Channel Geometry Estimation Module in SWAT

#### 2.4. Input Data for SWAT

#### 2.5. Streamflow and Water Quality Simulation Using SWAT

^{th}sub-watershed as shown in Figure 2. However, the calibration and validation for water quality was carried out for only one observation station, Nakbon B Station which is located nearby Dosan Station, due to the absence of available observed water quality data for Andong Dam Station. This study analyzed sediment, total nitrogen (TN), and DO among various water quality items.

#### 2.6. Model Calibration and its Evaluation

^{2}), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE)-observations standard deviation ratio (RSR), and percent bias (PBIAS). R

^{2}is the square of the coefficient of correlation between the simulated and observed values and it ranges from 0 to 1. When R

^{2}is 1, it means that the simulated data are perfect. NSE indicates the fit of the data to a linear 1:1 measured versus the simulated best-fit line [34], and it ranges from minus infinity (poor model) to 1.0 (perfect model). A limitation of NSE is the fact that larger values in a time series are significantly overestimated while lower ones are neglected because the differences between the observed and simulated values are computed as squared values [31].

## 3. Results and Discussion

#### 3.1. Comparison between Current and New Regression Equations for Applicability Assessment of the Middle-Scale Watershed

^{2}); and ${W}_{btm,fld}$ is the bottom width of the floodplain (m). Figure 5 shows the comparisons of the average measured bankfull width of the main channels and the floodplain bottom width of each sub-watershed constructed through Google Earth Pro, the regression equations of the current SWAT model, and the new regression equations. Figure 6 shows the bar graphs that indicate the results of the calculated main channel width and floodplain bottom width for each sub-watershed using the current SWAT regression equations and the new regression equations. It can be seen in Figure 5 that SWAT tended to slightly overestimate the bankfull width of the main channels. In particular, the bottom width of the floodplain was significantly overestimated when compared to the measured data. On the contrary, the new regression equations showed a high degree of accuracy for both results compared with the current regression equations in SWAT. According to the analysis of the channel geometry estimation results of S1 (current regression equations) and S2 (new regression equations), the channel bankfull width and the floodplain bottom width of S1 were estimated to be 35% and 73% larger, respectively, than those of S2, for all of the sub-watersheds. These results reflect that the current regression equations in SWAT are inappropriate for middle-scale watersheds such as the Andong Dam because of overestimation as Ames et al. [6] stated. Specifically, the current regression equation of SWAT for estimating floodplain widths was found to be considerably inaccurate.

#### 3.2. Model Performance Evaluation and Analysis of the Effect of Channel Geometry Data on Model Simulation

#### 3.2.1. Comparison of Flow Velocity Estimation Results from Current and New Regression Equations.

#### 3.2.2. Comparison of Simulated Streamflow for Scenarios 1 and 2

_{stored}is the storage volume, K is the storage time constant, X is the weighting factor, q

_{in}is the inflow, q

_{out}is the flow out, ${L}_{ch}$ is the channel length (km), and ${v}_{c}$ is the flow velocity. Here, K is inversely proportional to the flow velocity and is proportional to the channel length (Equation (11)) [7]. Therefore, if the channel width is relatively larger like in S1, the flow velocity will be estimated to be smaller, and thus, K will be calculated to be larger so that the amount of water stored in the channel will increase. This means that the streamflow or outflow from the channel decreases because more water is stored by the channel when the channel width increases. This trend, where the peak flow as well as the falling limb of S1 showed that the higher values were smaller than those of S2 due to the difference between the channel widths, can be easily seen in all hydrographs in Figure 8.

^{2}, NSE, RSR, and PBIAS for the calibration and validation periods. According to the classification of model efficiencies (Table 4), the values of R

^{2}and NSE during the calibration periods at both stations were ‘very good’ and those during the validation periods were ‘good’. The values of the RSR of S1 and S2 during the calibration period at Dosan Station were ‘good’ and ‘very good’, respectively, and those values were ‘satisfactory’ during validation. At Andong Dam Station, all values of RSR of S1 and S2 during the calibration and validation were ‘good’. Overall, although there was a slight difference in R

^{2}, NSE, and RSR between S1 and S2 at both stations, no specific differences were indicated. This is because the amount of water flowing into the channel was calculated as the sum of the surface runoff, lateral flow, and baseflow at the watershed scales, which was estimated using meteorological and topographical data before flowing into the channel. In other words, the parameters related to the cross-sectional shape of the channel had little effect on the flow rate calculation for the channel.

#### 3.2.3. Comparison of Simulated Water Quality for Scenarios 1 and 2

^{2}, NSE, RSR, and PBIAS of S2 for the calibration results improved more compared with ‘satisfactory’ for Sediment, TN, and DO as shown in Table 6. On the other hand, NSE, RSR, and PBIAS of S1 for TN during the calibration period were within the ‘unsatisfactory’ range. During the validation periods, the simulation results of S1 and S2 improved more than the ‘satisfactory’ ranges apart from DO. Although the DO simulation results indicated unsatisfactory, S2 showed the higher values for all model evaluation indicators compared to S1. This suggests that the water quality simulation was influenced by the channel geometry information when considering the same parameters and ranges were identically applied to S1 and S2 for calibration.

#### 3.3. Modification of the SWAT 2012 Code to Apply Various Shapes of Channel Cross-Sections

_{fld,left}and W

_{fld,right}are the left and right side width of the floodplain; slp

_{fld,left}and slp

_{fld,right}are the side slope of the floodplain; and slp

_{ch,left}and slp

_{ch,right}are the slope of the main channel. The cross-sectional shape input table should use the new improved channel geometry module. If the user has actual channel data on the cross section, the channel shape can be accurately reflected through the newly added variables. Furthermore, the convenience, when exploiting the modified module, was improved by adjusting the SWAT so that the user could select the current module or an improved one. In addition, the SWAT 2012 code was modified to display the sub-watershed name where the river cross-section data that users generated were applied when using the modified SWAT, which helps the user to see if cross-section information is applied to each sub-watershed.

## 4. Conclusions

^{2}, NSE, RSR, and PBIAS) indicated greater values than those applying the current equations. The simulation results of S2 replicated the observed water quality data better by predicting peak values to be higher than those of S1. Moreover, the analysis of LDCs developed by the results of S1 and S2 suggested that the application of faulty channel geometry information to the channel not only leads to the uncertainty of the water quality simulation but also to the erroneous analysis of water quality condition. Therefore, it was confirmed that the accurate channel cross-section data are as critical as meteorological and topographical input data.

## Author Contributions

## Acknowledgments

## Conflicts of Interest

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**Figure 3.**An example of a comparison of the top width of the main channel according to shooting time (from Google Earth Pro).

**Figure 5.**Comparisons between measured data and the calculated results from the current equations and the new equations. (

**a**) Bankfull stage width of the main channel. (

**b**) Bottom width of the floodplain.

**Figure 6.**Results of channel geometry estimation for each sub-watershed. (

**a**) Bankfull width of the main channel for each sub-watershed in the Andong Dam watershed. (

**b**) Floodplain bottom width for each sub-watershed in the Andong Dam watershed.

**Figure 7.**Flow velocity estimation results for Dosan Station and Andong Dam Station. (

**a**) Dosan Station (No. 25 sub-watershed). (

**b**) Andong Dam Station (No. 34 sub-watershed).

**Figure 8.**Hydrographs during summer and fall seasons of each year. (

**a**,

**c**,

**e**) Dosan Station and (

**b**,

**d**,

**f**) Andong Dam Station.

**Figure 9.**Comparisons of water quality simulation results. (

**a**) Sediment during the calibration period. (

**b**) Sediment during the validation period. (

**c**) TN during the calibration period. (

**d**) TN during the validation period. (

**e**) DO during the calibration period. (

**f**) DO during the validation period.

**Figure 10.**Load duration curve of sediment for the simulation periods (2011–2015). (

**a**) Dosan Station. (

**b**) Andong Dam Station.

No. | Area (km^{2}) | Ch_w (m) | Fld_w (m) | No. | Area (km^{2}) | Ch_w (m) | Fld_w (m) |
---|---|---|---|---|---|---|---|

1 | 54.1 | 8.1 | 20.0 | 18 | 545.2 | 34.2 | 68.4 |

2 | 41.5 | 8.3 | 17.9 | 19 | 104.4 | 18.3 | 37.1 |

3 | 134.8 | 14.4 | 30.2 | 20 | 141.0 | 22.0 | 37.4 |

4 | 59.2 | 7.8 | 18.8 | 22 | 661.4 | 43.8 | 66.9 |

5 | 201.2 | 15.7 | 39.9 | 25 | 169.9 | 17.7 | 32.6 |

6 | 49.3 | 8.5 | 21.1 | 26 | 702.0 | 45.6 | 75.2 |

8 | 42.9 | 8.2 | 18.8 | 27 | 754.7 | 39.2 | 82.6 |

9 | 260.8 | 21.5 | 43.6 | 28 | 90.8 | 9.3 | 24.6 |

11 | 50.4 | 12.4 | 19.8 | 30 | 197.6 | 19.4 | 35.7 |

12 | 28.6 | 9.3 | 21.4 | 33 | 41.0 | 6.1 | 12.8 |

13 | 362.1 | 23.8 | 54.1 | 34 | 85.2 | 15.4 | 31.2 |

14 | 35.9 | 7.1 | 21.8 | 35 | 39.3 | 9.6 | 23.3 |

15 | 404.7 | 26.4 | 56.2 | 40 | 1187.5 | 62.5 | 89.0 |

16 | 57.0 | 13.2 | 27.6 |

Parameter | Definition | Variation Method | Fitted Value | |||
---|---|---|---|---|---|---|

Dosan | Andong Dam | |||||

S1 | S2 | S1 | S2 | |||

ALPHA_BF | Baseflow alpha factor | Replace | 0.61 | 0.61 | 0.83 | 0.83 |

CH_K2 | Effective hydraulic conductivity in main channel alluvium | Replace | 13.34 | 13.47 | 0.77 | 0.78 |

CN2 | SCS runoff curve number | Multiply | 0.82 | 0.83 | 0.75 | 0.76 |

ESCO | Soil evaporation compensation factor | Replace | 0.45 | 0.46 | 0.09 | 0.09 |

GW_DELAY | Groundwater delay time | Replace | 113 | 114 | 269 | 271 |

GWQMN | Threshold depth of water in the shallow aquifer required for return flow to occur | Replace | 2451 | 2476 | 822 | 830 |

LAT_TTIME | Lateral flow travel time | Replace | 3.16 | 3.19 | 1.45 | 1.47 |

MSK_CO1 | Calibration coefficient used to control impact of the storage time constant for normal flow | Replace | 4.03 | 4.03 | 4.03 | 4.03 |

MSK_CO2 | Calibration coefficient used to control impact of the storage time constant from low flow | Replace | 3.94 | 3.94 | 3.94 | 3.94 |

MSK_X | Weighting factor controlling relative importance of inflow rate and outflow rate in determining water storage in a reach | Replace | 0.01 | 0.01 | 0.01 | 0.01 |

SOL_AWC | Available water capacity of the soil layer | Multiply | 0.83 | 0.84 | 1.24 | 1.25 |

SURLAG | Surface runoff lag time | Replace | 6.65 | 6.72 | 6.65 | 6.72 |

Parameter | Definition | Variation Method | VALUES | |
---|---|---|---|---|

S1 | S2 | |||

CDN | Denitrification exponential rate coefficient | Replace | 2.63 | 2.51 |

N_UPDIS | Nitrogen uptake distribution parameter | Replace | 16.70 | 38.10 |

PRF_BSN | Peak rate adjustment factor for sediment routing in the main channel | Replace | 0.46 | 0.21 |

ADJ_PKR | Peak rate adjustment factor for sediment routing in tributary | Replace | 1.13 | 0.62 |

RSDCO | Residue decomposition coefficient | Replace | 0.06 | 0.07 |

SOL_NO3 | Initial NO3 concentration in the soil layer | Replace | 7.90 | 27.50 |

LAT_ORGN | Organic N in the baseflow | Replace | 0.05 | 0.45 |

HLIFE_NGW | Half-life of nitrate in the shallow aquifer | Replace | 56.66 | 77.80 |

SLSUBBSN | Average slope length | Multiply | 1.23 | 0.86 |

HRU_SLP | Average slope steepness | Multiply | 1.03 | 1.24 |

USLE_P | USLE equation support practice | Replace | 0.35 | 0.84 |

BIOMIX | Biological mixing efficiency | Replace | 0.14 | 0.16 |

USLE_C | Min value of USLE C factor applicable to the land cover/plant | Multiply | 1.19 | 1.03 |

USLE_K | USLE equation soil erodibility (K) factor | Multiply | 1.45 | 1.30 |

AI1 | Fraction of algal biomass that is nitrogen | Replace | 0.07 | 0.08 |

Performance Rating | R^{2}, NSE | RSR | PBIAS | ||
---|---|---|---|---|---|

Streamflow | Sediment | N, P | |||

Very good | 0.75$\u2013$1.00 | 0.00$\u2013$0.50 | $\u2013\pm $10 | $\u2013\pm $15 | $\u2013\pm $25 |

Good | 0.50$\u2013$0.75 | 0.50$\u2013$0.60 | $\pm $10 $\u2013\pm $15 | $\pm $15 $\u2013\pm $30 | $\pm $25 $\u2013\pm $40 |

Satisfactory | 0.25$\u2013$0.50 | 0.60$\u2013$0.70 | $\pm $15 $\u2013\pm $25 | $\pm $30 $\u2013\pm $55 | $\pm $40 $\u2013\pm $70 |

Unsatisfactory | 0.00$\u2013$0.25 | 0.70$\u2013$ | $\pm $25 $\u2013$ | $\pm $55 $\u2013$ | $\pm $70 $\u2013$ |

Criteria | Dosan Station | Andong Dam Station | ||||||
---|---|---|---|---|---|---|---|---|

Calibration | Validation | Calibration | Validation | |||||

S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | |

R^{2} | 0.79 | 0.78 | 0.61 | 0.62 | 0.78 | 0.77 | 0.69 | 0.70 |

NSE | 0.77 | 0.77 | 0.52 | 0.54 | 0.74 | 0.74 | 0.67 | 0.69 |

RSR | 0.51 | 0.48 | 0.68 | 0.69 | 0.51 | 0.51 | 0.57 | 0.56 |

PBIAS | 29.7 | 16.3 | 36.0 | 31.2 | 24.9 | 14.8 | 35.0 | 20.7 |

Criteria | Sediment | TN | DO | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Calibration | Validation | Calibration | Validation | Calibration | Validation | |||||||

S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | |

R^{2} | 0.65 | 0.87 | 0.84 | 0.84 | 0.65 | 0.76 | 0.71 | 0.72 | 0.79 | 0.79 | 0.21 | 0.32 |

NSE | 0.65 | 0.87 | 0.78 | 0.80 | 0.41 | 0.65 | 0.32 | 0.41 | 0.59 | 0.62 | −0.49 | 0.21 |

RSR | 0.59 | 0.36 | 0.46 | 0.44 | 0.77 | 0.59 | 0.46 | 0.31 | 0.64 | 0.62 | 1.03 | 1.00 |

PBIAS | −7.7 | −9.9 | 37.6 | 22.6 | 75.3 | 57.4 | 18.8 | 14.4 | 62.3 | 49.9 | 95.8 | 66.6 |

Values | Sediment | TN | DO | |||
---|---|---|---|---|---|---|

S1 | S2 | S1 | S2 | S1 | S2 | |

5-year sum (tons) | 256,000 | 309,000 | 2427 | 4396 | 11,142 | 14,334 |

Percent difference | 20.8% | 81.1% | 28.6% |

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## Share and Cite

**MDPI and ACS Style**

Han, J.; Lee, D.; Lee, S.; Chung, S.-W.; Kim, S.J.; Park, M.; Lim, K.J.; Kim, J. Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed. *Water* **2019**, *11*, 718.
https://doi.org/10.3390/w11040718

**AMA Style**

Han J, Lee D, Lee S, Chung S-W, Kim SJ, Park M, Lim KJ, Kim J. Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed. *Water*. 2019; 11(4):718.
https://doi.org/10.3390/w11040718

**Chicago/Turabian Style**

Han, Jeongho, Dongjun Lee, Seoro Lee, Se-Woong Chung, Seong Joon Kim, Minji Park, Kyoung Jae Lim, and Jonggun Kim. 2019. "Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed" *Water* 11, no. 4: 718.
https://doi.org/10.3390/w11040718