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

Table 3

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

Daily maximum 𝑇𝑎-𝑇𝑎							Daily minimum 𝑇𝑎-𝑇𝑎
Sta_Pairs*	DataNum*	𝑎 *	𝑏 *	𝑅^2	MAE (°C)	RMSE (°C)	DataNum*	𝑎 *	𝑏 *	𝑅^2	MAE (°C)	RMSE (°C)
1-1N	14328	1.01	0.16	0.98	1.74	2.41	14321	1.02	0.33	0.97	2.11	2.85
2-2N	13851	0.97	−0.31	0.98	1.61	2.23	13859	0.99	−4.07	0.96	2.73	3.53
3-3N	8640	1.02	−1.09	0.98	1.94	2.62	8641	1.01	1.61	0.97	2.56	3.51
4-4N	13980	0.96	0.22	0.98	1.50	2.12	13981	0.83	1.28	0.97	1.95	2.54
5-5N	13470	0.97	−0.14	0.98	1.52	2.13	13473	0.98	−0.83	0.98	1.57	2.33
6-6N	12851	0.93	−3.16	0.98	1.86	2.47	12849	0.95	−3.10	0.97	2.38	3.25
7-7N	13769	1.02	−0.33	0.99	1.19	1.71	13770	1.00	−0.99	0.98	1.50	2.05
8-8N	14537	0.98	1.03	0.99	1.24	1.80	14535	1.04	−0.06	0.98	1.77	2.43
Average		0.98	−0.45	0.98	1.58	2.19		0.98	−0.73	0.97	2.07	2.81

∗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.