# Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China

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## Abstract

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## 1. Introduction

^{2}correlation coefficient of the annual average runoff simulation value, and the measured value increased from 0.70 to 0.86 [25]. Other scholars have improved the simulation accuracy by using an energy balance snowmelt model instead of the temperature index model for maritime regions [26]. It is noteworthy that when performing a SWAT runoff simulation, we should consider that the time settings of the maximum and minimum snow melting factors in the source factor formula are according to the empirical values for North America. Thus, when SWAT is applied to another study area, the relevant parameters should be appropriately optimized. Therefore, taking advantage of its free and open-source nature, which allows the code of SWAT model to be fine-tuned [27], according to the snowfall and snowmelt characteristics in alpine regions of northern China, the maximum and minimum snowmelt factor times in the study area can be determined by a baseflow segmentation method to improve the simulation accuracy.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. The Hulan River is a primary tributary on the left bank of the Songhua River and is located between 125°55′ and 128°43′ E and 45°52′ and 48°03′ N in the central part of Heilongjiang Province. The river originates from Xiaoxing’anling, and the terrain of the Hulan River Basin is high in the northeast and low in the southwest, changing from low hills and high plains to valley plains with less undulation. The terrain of the basin is fan-shaped, and the northeast region is a mountainous area that belongs to the Xiaoxing’anling Mountains with dense forests. The watershed elevation is between 300 and 1000 m. The western and central parts are hilly terraces with elevations between 200 and 300 m and a ground slope of approximately 1/20–1/200; the southern part is low, with elevations between 120 and 200 m and a ground slope of approximately 1/200–1/3000. The whole terrain is inclined from northeast to southwest. The mountainous area, the hilly area and the plains area account for 37%, 22% and 41% of the basin area, respectively.

#### 2.2. SWAT Model Description

#### 2.3. SWAT Snow Melting Module Principle

#### 2.3.1. The Snowpack

_{i}and SNO

_{i−}

_{1}are the snow equivalents of the current day (i) and the previous day (i − 1), respectively; R

_{sfi}is the snowfall equivalent of the current day; E

_{subi}is the evaporated snow equivalent of the current day and SNO

_{mlti}is the melted snow equivalent of the current day.

#### 2.3.2. The Snowmelt

_{2}O·°C

^{−1}·d

^{−1}) and i represents the order of days in the year.

#### 2.4. Model Modifications for the Snowmelt Runoff

#### 2.4.1. Determination of the Snowmelt Runoff Period Based on Baseflow Segmentation

_{t}= f

_{1}q

_{t−1}+ [(1 + f

_{1})/2](Q

_{t}− Q

_{t−1}),

_{t}) is determined by:

_{t}= Q

_{t}− q

_{t},

_{t}and q

_{t}

_{−1}are the surface runoff at times t and t − 1, respectively; Q

_{t}and Q

_{t−1}are the total runoff at times t and t − 1, respectively; and f

_{1}is the filter parameter affecting the attenuation of the baseflow. Related research showed that to improve the accuracy of the calculation, the data can be filtered repeatedly a certain number (N) of times [29].

_{t}> 0, q

_{t}> 0, Q

_{t}≥ b

_{t}, and Q

_{t}≥ qt. During the course of the operation, if b

_{t}< 0, then b

_{t}= 0, and q

_{t}= Q

_{t}; if b

_{t}> Q

_{t}, then b

_{t}= Q

_{t}; and if q

_{t}< 0, then q

_{t}= 0, and b

_{t}= Q

_{t}.

_{t}is the baseflow and Q

_{t}is the total runoff.

#### 2.4.2. Modifying the Code of the Snow Melting Module of SWAT

#### 2.5. Model Calibration and Validation

#### 2.6. Statistical Evaluation

^{2}) between the observations and the best final simulations. The NSE (Equation (11)) determines the relative magnitude of the residual variance compared to that of the observed data variance. The NSE ranges between −∞ and 1.0, with NSE = 1.0 being the optimal value. Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance, whereas values ≤0.0 indicate that the mean observed value is a better predictor than the simulated value, which means unacceptable performance [38]. The PBIAS (Equation (12)) is a statistical error index widely used for model performance evaluation. This index measures the average tendency of the simulated data to be larger or smaller than their observed counterparts. The optimal value of PBIAS is 0.0, with positive values indicating model overestimation and negative values indicating model underestimation. Linear regression is used for model fitting, and the coefficient of determination (R

^{2}) is used for evaluating the performance. A well-performing model generally has an R

^{2}value close to 1. Typically, values greater than 0.5 are considered acceptable [38]. The performance accuracy of each simulation is assessed by comparison with the observed data.

## 3. Results

#### 3.1. Model Modification

_{1}= 0.85 [29]. The snowmelts occur during a period of 3–4 months according to temperature changes and experience gained over the years. Therefore, the change in the baseflow segmentation index from March 1 to April 30 is shown. It can be judged that the sudden change (decrease) in the baseflow index during the 3–4 months indicates that the underground-dominated runoff segmentation transforms into snowmelt-involved runoff segmentation. Therefore, the start time of the runoff snow melting, namely, the date of the minimum runoff melting factor, is determined. From the temperature curve, the highest temperature or the lowest temperature of the mutation date is close to 0 °C, which further verifies the feasibility of the baseflow segmentation method to determine the snowmelt time (Figure 2).

#### 3.2. Model Setup and Initial Simulation

#### 3.3. Sensitivity Analysis

_{i}is the calibration parameter and m is the number of parameters considered (set to 21). The smaller the P-value is, the larger the absolute value of t-Stat is, and the more sensitive the parameter is [22]. Due to a bug in SWAT-CUP, the SNO50cov.bsn parameter cannot exceed 0.918999. While this factor is important for the snowmelt module, it is listed as a parameter for manual tuning and is not included in the sensitivity analysis.

## 4. Discussion

#### 4.1. Sensitivity Analysis

#### 4.2. Calibration and Validation

#### 4.3. Model Performance Assessment

#### 4.4. Adequacy of the Modification

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Flanagan, D.C.; Nearing, M.A. USDA-Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation. NSERL Report 10; USDA-ARS National Soil Erosion Research Laboratory: West Lafayette, IN, USA, 1995.
- Wang, X.; Melesse, A.M. Evaluation of the Swat Model’s Snowmelt Hydrology in a Northwestern Minnesota Watershed. Trans. Asae
**2005**, 48, 1359. [Google Scholar] [CrossRef] - Zeinivand, H.; Smedt, F.D. Hydrological modeling of snow accumulation and melting on river basin scale. Water Resour. Manag.
**2009**, 23, 2271–2287. [Google Scholar] [CrossRef] - Shen, Y.J.; Shen, Y.; Fink, M.; Kralisch, S.; Chen, Y.; Brenning, A. Trends and variability in streamflow and snowmelt runoff timing in the southern Tianshan Mountains. J. Hydrol.
**2018**, 557, 173–181. [Google Scholar] [CrossRef] - Li, H.Y.; Wang, Y.X.; Jia, L.N.; Wu, Y.N.; Xie, M. Runoff characteristics of the Nen River Basin and its cause. J. Mt. Sci.
**2014**, 11, 110–118. [Google Scholar] [CrossRef] - Tuo, Y.; Marcolini, G.; Disse, M.; Chiogna, G. Calibration of snow parameters in SWAT: Comparison of three approaches in the Upper Adige River basin (Italy). Hydrol. Sci. J.
**2018**, 63, 657–678. [Google Scholar] - Debele, B.; Srinivasan, R.; Gosain, A. Comparison of process-based and temperature-index snowmelt modeling in SWAT. Water Resour. Manag.
**2010**, 24, 1065–1088. [Google Scholar] [CrossRef] - USGS. Precipitation Runoff Modeling System (PRMS); John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 1983; pp. 206–207.
- Band, L.E.; Patterson, P.; Nemani, R.; Running, S.W. Forest ecosystem processes at the watershed scale: Incorporating hillslope hydrology. Agric. For. Meteorol.
**1993**, 63, 93–126. [Google Scholar] [CrossRef] [Green Version] - Wood, E.F.; Lettenmaier, D.P.; Zartarian, V.G. A Land Surface Hydrology Parameterization with Sub-Grid Variability for General Circulation Models. J. Geophys. Res. D
**1992**, 97, 2717–2728. [Google Scholar] [CrossRef] - Gassman, P.W.; Sadeghi, A.M.; Srinivasan, R. Applications of the SWAT model special section: Overview and insights. J. Environ. Qual.
**2014**, 43, 1–8. [Google Scholar] [CrossRef] - Douglasmankin, K.R.; Srinivasan, R.; Arnold, J.G. Soil and Water Assessment Tool (SWAT) Model: Current Developments and Applications. ASABE
**2010**, 53. [Google Scholar] [CrossRef] - Gassman, P.W.; Reyes, M.R.; Green, C.H.; Arnold, G.J. The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Trans. ASABE
**2007**, 50, 1211–1250. [Google Scholar] [CrossRef] [Green Version] - Lévesque, É.; Anctil, F.; Griensven, A.V.; Beauchamp, N. Evaluation of streamflow simulation by SWAT model for two small watersheds under snowmelt and rainfall. Hydrol. Sci. J.
**2008**, 53, 961–976. [Google Scholar] - Arnold, J.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrologic modeling and assessment part I: Model development. Jawra J. Am. Water Resour. Assoc.
**1998**, 34, 73–89. [Google Scholar] [CrossRef] - Wang, X.; Melesse, A.M. Effects of Statsgo and Ssurgo as Inputs on Swat Model’s Snowmelt Simulation 1. Jawra J. Am. Water Resour. Assoc.
**2006**, 42, 1217–1236. [Google Scholar] [CrossRef] - Bouraoui, F.; Grizzetti, B. Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture. Sci. Total Environ.
**2014**, 468, 1267–1277. [Google Scholar] [CrossRef] - Chen, Y.; Marek, G.; Marek, T.; Brauer, D.; Srinivasan, R. Improving SWAT auto-irrigation functions for simulating agricultural irrigation management using long-term lysimeter field data. Environ. Model. Softw.
**2018**, 99, 25–38. [Google Scholar] [CrossRef] - Wu, Y.; Liu, J.; Shen, R.; Fu, B. Mitigation of nonpoint source pollution in rural areas: From control to synergies of multi ecosystem services. Sci. Total Environ.
**2017**, 607, 1376–1380. [Google Scholar] [CrossRef] - Francesconi, W.; Srinivasan, R.; Pérez-Miñana, E.; Willcock, S.P.; Quintero, M. Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review. J. Hydrol.
**2016**, 535, 625–636. [Google Scholar] [CrossRef] - Golmohammadi, G.; Rudra, R.; Dickinson, T.; Goel, P.; Veliz, M. Predicting the temporal variation of flow contributing areas using SWAT. J. Hydrol.
**2017**, 547, 375–386. [Google Scholar] [CrossRef] - Liu, R.; Xu, F.; Zhang, P.; Yu, W.; Men, C. Identifying non-point source critical source areas based on multi-factors at a basin scale with SWAT. J. Hydrol.
**2016**, 533, 379–388. [Google Scholar] [CrossRef] - Malagò, A.; Efstathiou, D.; Bouraoui, F.; Nikolaidis, N.P.; Franchini, M.; Bidoglio, G.; Kritsotakis, M. Regional scale hydrologic modeling of a karst-dominant geomorphology: The case study of the Island of Crete. J. Hydrol.
**2016**, 540, 64–81. [Google Scholar] [CrossRef] - Wang, Y.; Bian, J.; Wang, S.; Tang, J.; Ding, F. Evaluating SWAT Snowmelt Parameters and Simulating Spring Snowmelt Nonpoint Source Pollution in the Source Area of the Liao River. Pol. J. Environ. Stud.
**2016**, 25. [Google Scholar] [CrossRef] - Fontaine, T.A.; Cruickshank, T.S.; Arnold, J.G.; Hotchkiss, R.H. Development of a snowfall–snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). J. Hydrol.
**2002**, 262, 209–223. [Google Scholar] [CrossRef] - Qi, J.; Li, S.; Jamieson, R.; Hebb, D.; Xing, Z.; Meng, F.-R. Modifying SWAT with an energy balance module to simulate snowmelt for maritime regions. Environ. Model. Softw.
**2017**, 93, 146–160. [Google Scholar] [CrossRef] - Holvoet, K.; Griensven, A.V.; Gevaert, V.; Seuntjens, P.; Vanrolleghem, P.A. Modifications to the SWAT code for modelling direct pesticide losses. Environ. Model. Softw.
**2008**, 23, 72–81. [Google Scholar] [CrossRef] - Aksoy, H.; Kurt, I.; Eris, E. Filtered smoothed minima baseflow separation method. J. Hydrol.
**2009**, 372, 94–101. [Google Scholar] [CrossRef] - Nathan, R.J.; McMahon, T.A. Evaluation of automated techniques for baseflow and recession analyses. Water Resour. Res.
**1990**, 26, 1465–1473. [Google Scholar] [CrossRef] - Sloto, R.A.; Crouse, M.Y. HYSEP, A Computer Program for Streamflow Hydrograph Separation and Analysis; U.S. Geological Survey: Reston, VA, USA, 1996.
- Arnold, J.G.; Moriasi, D.N.; Gassman, P.W.; Abbaspour, K.C.; White, M.J.; Srinivasan, R.; Santhi, C.; Harmel, R.D.; Griensven, A.V.; Liew, M.W.V. SWAT: Model use, calibration, and validation. Trans. Asabe
**2012**, 55, 1549–1559. [Google Scholar] [CrossRef] - Neitsch, S.; Arnold, J.; Kiniry, J.; Srinivasan, R.; Williams, J. Soil and water assessment tool user’s manual version 2000. Gsl. Rep.
**2002**, 202, 2–6. [Google Scholar] - Srinivasan, R.; Zhang, X.; Arnold, J. SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin. Trans. Asabe
**2010**, 53, 1533–1546. [Google Scholar] [CrossRef] - Holzworth, D.P.; Snow, V.; Janssen, S.; Athanasiadis, I.N.; Donatelli, M.; Hoogenboom, G.; White, J.W.; Thorburn, P. Agricultural production systems modelling and software: Current status and future prospects. Environ. Model. Softw.
**2015**, 72, 276–286. [Google Scholar] [CrossRef] - Abbaspour, K.C. SWAT-CUP 2012: SWAT Calibration and Uncertainty Programs—A User Manual; Eawag: Dübendorf, Switzerland, 2013. [Google Scholar]
- Dhami, B.; Himanshu, S.K.; Pandey, A.; Gautam, A.K. Evaluation of the SWAT model for water balance study of a mountainous snowfed river basin of Nepal. Environ. Earth Sci.
**2018**, 77, 21. [Google Scholar] [CrossRef] - Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Moriasi, D.N.; Arnold, J.G.; Liew, M.W.V.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. Asabe
**2007**, 50, 885–900. [Google Scholar] [CrossRef] - Singh, V.; Bankar, N.; Salunkhe, S.S.; Bera, A.K.; Sharma, J. Hydrological stream flow modelling on Tungabhadra catchment: Parameterization and uncertainty analysis using SWAT CUP. Curr. Sci.
**2013**, 104, 1187–1199. [Google Scholar]

**Figure 2.**Baseflow segmentation index and temperature trend of the Qinjia station (Green arrows represent snowmelt starting date, T-max is the daily maximum temperature and T-min is the daily minimum temperature).

**Figure 6.**Comparison of two simulation results and observed values (Date from March 1 to April 30 of each year from 2010 to 2016; OS represents the original simulation and MS represents the modified simulation).

N Total | Mean | Standard Deviation | Sum | Minimum | Median | Maximum |
---|---|---|---|---|---|---|

9 | 89 | 6.667 | 803 | 80 | 89 | 99 |

Data | Range Accuracy | Data Sources |
---|---|---|

Digital elevation model | STRM 90 m | http://www.gscloud.cn/ |

Soil maps | 1:1000000 | Harmonized world soil database |

Land use/cover | 1:100000 | http://www.resdc.cn/data.aspx?DATAID=99 |

Weather data | CMADS (2008–2016) | http://westdc.westgis.ac.cn |

Runoff | 2008–2016 | Hydrographic office |

Parameter_Name | File | Physical Significance | Range | Unit |
---|---|---|---|---|

CN2 | mgt | SCS runoff curve number | 35–98 | dimensionless |

ALPHA_BF | gw | Baseflow alpha factor | 0–1 | days |

GW_DELAY | gw | Groundwater delay | 0–500 | days |

GWQMN | gw | Threshold depth of water in the shallow aquifer required for return flow to occur | 0–5000 | mm H_{2}O |

GW_REVAP | gw | Groundwater “revap” coefficient | 0.02–0.2 | dimensionless |

REVAPMN | gw | Threshold depth of water in the shallow aquifer for “revap” to occur | 0–500 | mm H_{2}O |

ESCO | hru | Soil evaporation compensation factor | 0–1 | dimensionless |

CANMX | hru | Maximum canopy storage | 0–100 | mm H_{2}O |

SLSUBBSN | hru | Average slope length | 10–150 | m |

SOL_K(..) | sol | Saturated hydraulic conductivity | 0–2000 | mm/hr |

SOL_BD(..) | sol | Moist bulk density | 0.9–2.5 | g/cm^{3} |

SOL_AWC(..) | sol | Available water capacity of the soil layer | 0–1 | mm H_{2}O/mmsoil |

ALPHA_BNK | rte | Baseflow alpha factor for bank storage | 0–1 | days |

CH_K2 | rte | Effective hydraulic conductivity in main channel alluvium | −500.01 | mm/hr |

CH_N2 | rte | Manning’s “n” value for the main channel | −0.31 | dimensionless |

SFTMP | bsn | Snowfall temperature | −40 | °C |

SMTMP | bsn | Snow melt base temperature | −40 | °C |

SMFMX | bsn | Maximum melt rate for snow during year (occurs on summer solstice) | 0–20 | mm H2O/°C day |

TIMP | bsn | Snowpack temperature lag factor | 0–1 | dimensionless |

SNOCOVMX | bsn | Minimum snow water content that corresponds to 100% snow cover | 0–500 | mm H_{2}O |

SMFMN | bsn | Minimum melt rate for snow during the year (occurs on winter solstice) | 0–20 | mm H_{2}O/°C day |

SNO50COV | bsn | Minimum snow water content that corresponds to 50% snow cover | 0–500 | dimensionless |

Parameter_Name | file | Assignment | Fitted_Value | Unit |
---|---|---|---|---|

CN2 | mgt | v | 35.2326 | dimensionless |

ALPHA_BF | gw | v | 0.502689 | days |

GW_DELAY | gw | v | 65.853645 | days |

GWQMN | gw | v | 841.039673 | mm H_{2}O |

GW_REVAP | gw | v | 0.098768 | dimensionless |

REVAPMN | gw | v | 54.239353 | mm H_{2}O |

ESCO | hru | v | 0.157893 | dimensionless |

CANMX | hru | v | 41.954502 | mm H_{2}O |

SLSUBBSN | hru | v | 42.100601 | m |

SOL_K(..) | sol | v | 1785.92395 | mm/hr |

SOL_BD(..) | sol | v | 1.465848 | g/cm^{3} |

SOL_AWC(..) | sol | v | −0.101126 | mm H_{2}O/mmsoil |

ALPHA_BNK | rte | v | 0.503674 | days |

CH_K2 | rte | v | 344.855682 | mm/hr |

CH_N2 | rte | v | 0.219884 | dimensionless |

SFTMP | bsn | v | 4.236598 | °C |

SMTMP | bsn | v | 8.508638 | °C |

SMFMX | bsn | v | 6.828101 | mm H_{2}O/°C day |

TIMP | bsn | v | 0.545052 | dimensionless |

SNOCOVMX | bsn | v | 79.400223 | mm H_{2}O |

SMFMN | bsn | v | 15.326536 | mm H_{2}O/°C day |

SNO50COV | bsn | v | 0.5 | dimensionless |

Annual | Mode Evaluation Statistics | Original | Modification | ||

Calibration (2010–2013) | Validation (2014–2016) | Calibration (2010–2013) | Validation (2014–2016) | ||

NSE | 0.6926 | 0.70243 | 0.70253 | 0.813204 | |

R^{2} | 0.7661 | 0.785 | 0.784 | 0.791 | |

PBIAS | −0.01732 | 0.0206 | −0.03299 | 0.011 | |

Snowmelt from March to April | Mode Evaluation Statistics | Original | Modification | ||

Calibration (2010–2013) | Validation (2014–2016) | Calibration (2010–2013) | Validation (2014–2016) | ||

NSE | −0.09896 | −3.396 | 0.141611 | 0.207441 | |

R^{2} | 0.225 | 0.013 | 0.347 | 0.231 | |

PBIAS | 0.73338 | 0.620122 | 0.557087 | 0.443954 |

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**MDPI and ACS Style**

Liu, Y.; Cui, G.; Li, H.
Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China. *Water* **2020**, *12*, 636.
https://doi.org/10.3390/w12030636

**AMA Style**

Liu Y, Cui G, Li H.
Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China. *Water*. 2020; 12(3):636.
https://doi.org/10.3390/w12030636

**Chicago/Turabian Style**

Liu, Yan, Geng Cui, and Hongyan Li.
2020. "Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China" *Water* 12, no. 3: 636.
https://doi.org/10.3390/w12030636