Improving the Performance of Temperature Index Snowmelt Model of SWAT by Using MODIS Land Surface Temperature Data

Table 5

Results of daily maximum and minimum LST linear regression analysis in each station pair.

Daily maximum LST							Daily minimum LST
Sta_Pairs*	DataNum*	𝑎 *	𝑏 *	𝑅^2	MAE (°C)	RMSE (°C)	DataNum	𝑎	𝑏	𝑅^2	MAE (°C)	RMSE (°C)
1-1N	826	0.95	−0.86	0.96	2.50	3.38	1305	1.00	−0.51	0.98	1.92	2.56
2-2N	644	0.90	−0.75	0.92	2.20	3.05	1182	0.91	−1.99	0.98	1.68	2.26
3-3N	742	1.01	−0.94	0.97	2.08	2.89	1323	1.03	1.96	0.98	1.77	2.32
4-4N	694	0.93	1.41	0.94	1.98	2.85	1192	0.88	−0.69	0.98	1.86	2.63
5-5N	978	0.94	0.22	0.97	2.21	2.98	1424	0.98	−1.36	0.99	1.36	1.80
6-6N	489	1.09	−2.80	0.90	2.70	3.75	1208	0.97	−5.25	0.97	2.28	2.98
7-7N	861	0.93	−2.85	0.93	3.09	3.89	1170	1.03	−1.69	0.97	1.85	2.51
8-8N	975	0.98	0.34	0.98	1.72	2.47	992	1.08	−0.81	0.97	2.32	3.04
Average		0.96	−0.78	0.94	2.31	3.16		0.98	−1.29	0.98	1.88	2.51

∗Sta_Pairs is the station pair name, DataNum is the amount of data used for the regression analysis, “𝑎” is the first order coefficient of linear regression equation, and the 𝑏 is the interception of linear regression equation.