# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 4 Results
### Calculation of Groundwater Recharge in the Jianusi Area by the SWAT Model
#### 4.2.2 Parameter Calibration and Verification  
Since the Jianusi region is located in the cold temperate zone and the WAT model has many parameters, the parameters with the highest correlation sensitivity coefficient were chosen to modify the model's parameters. The SWAT-CUP program was used to complete a sensitivity analysis of 22 parameters across two basins. Global sensitivity analysis was applied in this work. The model's integrated SU-F2 sampling technique was used to perform 500 iterative calculations after the parameters and the initial range of selected parameters were established [27]. The model's appropriateness was assessed using the Nash efficiency coefficient (NS) and determination coefficient (R\({}^{2}\)). Table 8 presents the ideal parameters. Figure 7: Relationship among groundwater recharge, precipitation and evapotranspiration. (ET represents annual average evapotranspiration, Q represents groundwater recharge, and P represents precipitation). The spreadsheet was rewritten, and the validation was rerun after the corrected parameters were re-entered into the model. Figure 8 displays the findings of the verification and calculation of the runoff rate. This time, the assessment parameters used were the Nash coefficient (NSE) and determination coefficient (R\({}^{2}\)), which are frequently employed in both local and international research. The better the effect, the closer the simulated value is to the observed value, the closer the secondary coefficient is to 1. Table 9 displays the reliability distribution under typical conditions. According to the experimental findings, the runoff simulation at the Tongjiang Hydrological Station was optimal when R\({}^{2}\) > 0.8 and NSE > 0.75. The runoff simulation's R\({}^{2}\) > 0.7 and NSE > 0.65 at the Fuyuan Hydrological Station were also good. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Encoding** & **Parameter Name** & **Parameter Meaning** & **Optimal Parameter** \\  & & **(Basin No. 1)** & **(Basin No. 2)** \\ \hline
1 & r\_CN2.mgt & SCS runoff curve value & 0.75 & 0.81 \\
2 & v\_GW\_DELAY.gw & Groundwater delay time & 784.90 & 721.10 \\  & & Level threshold of shallow aquifers & \\
3 & v\_GWQMN.gw & when groundwater enters the main & 2.01 & 1.88 \\  & & channel (mm) & \\
4 & v\_REVPMN.gw & Shallow groundwater evaporation & 900.40 & 855.50 \\
5 & v\_SOL\_AWC0.sol & Surface water availability (mm) & \(-\)0.44 & 1.02 \\
6 & v\_CH\_K2.rte & Effective permeability & 695.11 & 721.41 \\
7 & v\_RCHRG\_DP.gw & Permeability coefficient of deep aquifer & 0.55 & 0.53 \\
8 & r\_SOL\_K)().sol & Soil-saturated water & 1.60 & 1.45 \\
9 & r\_SOL\_ALB().sol & Moist soil albedo & 0.31 & 0.35 \\
10 & v\_ALPHA\_BNK.rte & Base flow regression constant & 0.24 & 0.24 \\
11 & v\_SLSUBBSN.hru & Average slope length (m) & 1.95 & 2.11 \\
12 & r\_HRU\_SLP.hru & Average slope (m/m) & 2.33 & 2.16 \\
13 & v\_CANMX.hru & Maximum canopy water storage (mm) & 427.5 & 455.4 \\
14 & v\_SFTMP.bsn & Average air temperature on snowfall & 10.0 & 9.12 \\  & & days (\({}^{\circ}\)C) & & \\
15 & v\_SMTMP.bsn & Average temperature on snowfall & 13.8 & 12.44 \\
16 & v\_SMFMX.bsn & Snowmelt factor & 35.0 & 45.50 \\
17 & v\_TMP.bsn & Temperature lag coefficient of & 2.84 & 2.58 \\
18 & v\_SNOCOVMX.bsn & Snow depth threshold/cm & 1002.29 & 800.50 \\
19 & v\_TLAPS.sub & Temperature lapse rate (\({}^{\circ}\)C/km) & 4.44 & 3.58 \\
20 & v\_ESCO.hru & Soil evaporation & 1.53 & 2.00 \\
21 & v\_EPCO.hru & Plant absorption & 0.71 & 0.85 \\  & & compensation coefficient & & \\
22 & v\_ALPHA\_BF.gw & Base flow alpha factor (1/day) & 1.25 & 1.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Sensitivity analysis table of parameters.