Simulation of snow covered area (SCA)

As demonstrated above in section 4.3.2, a threshold of 2 mm was applied to transform the simulated SWE by TRAIN model to snow cover extent. Figure 4.2 shows the comparisons of the modeled and observed snow cover by TRAIN and MODIS at the similar time in the winters of 2004, 2006 and 2008 in Rhineland-Palatinate. It illustrates that TRAIN model has relatively good performance in simulating the snow cover in this study area, though slight underestimation is suggested as compared to MODIS snow cover maps. Both the modeled and observed snow cover was mainly distributed in the mountainous regions, such as the Eifel Mountain in the northwest, the Hunsrück Mountain in the center and the Westerwald Mountain in the northeast.

Figure 4.3 depicts the daily time series of the simulated and observed snow covered area (SCA) by TRAIN and MODIS in the six snow seasons from 2002 to 2008. The comparisons suggest that TRAIN model could reconstruct the snow cover very well in most of the time, and the simulations had high consistency with the remotely sensed snow information from MODIS. All the recorded snowfall events during the six snow seasons were captured by TRAIN model. However, the modeled SCA was prone to be higher than MODIS SCA at the ablation phases of some snow events, e.g. February 2003 and March 2006. Raleigh et al. (2013) validated the MODIS snow cover products in a forest covered region using a ground-based monitoring network, and they concluded that MODIS data showed an underestimation bias of snow due to canopy obstruction in forests, though NDVI information has been involved into the snow mapping algorithm of MODIS snow products. About 42% of the study region is covered by forests, and this ratio is even higher in the highlands where snow more

frequently occurs. During the snow ablation phases, the intercepted snow on canopies unloads prior to the snowmelt on the ground because of the declined solar radiation in forests, but the remained snowpack under canopies is difficult to be detected by satellites. Thus, there are some uncertainties in judging whether TRAIN model has snow overestimation in the snow ablation phases. More in situ monitoring of snow processes, especially in the forest sites, is needed in the future.

Figure 4.2 Snow cover simulations by TRAIN model and MODIS snow maps in

Rhineland-Palatinate for 10 February 2004, 9 February 2006 and 4 February 2008.

Figure 4.3 Simulated SCA by TRAIN model and observed MODIS SCA in

To quantitatively assess the capability of TRAIN model in simulating snow cover, four evaluation indices of MAE, NSE, Bias and R as presented in section 4.3.3 were utilized to compare the simulated and observed SCA during the period 2002-2008. The four indices were calculated based on the SCA values derived from TRAIN model and MODIS snow products for each month from November to April and for the entire snow season. The results are demonstrated in Table 4.1, which indicates that the modeled SCA has overall MAE of 9.28%, NSE of 0.48, Bias of 5.92% and correlation coefficient (R) of 0.79 during the whole snow season (November to April), suggesting satisfactory performance in modeling the snow cover extent in the study area. However, the simulations from December to January showed lower accuracy as indicated by the smaller NSE values. The inconsistency between simulated and observed SCA in some snow ablation periods is an important reason. Table 4.1 illustrates that the modeled SCA has positive Bias values, especially in January and February. As discussed above, the bias might be partly led by the snow underestimation of MODIS in forest regions during the snow ablation processes.

Table 4.1 Evaluation indices of mean absolute error (MAE), Nash -Sutcliffe

efficiency scores (NSE), Bias and Correlation Coefficient (R) for simulated snow covered area (SCA) by TRAIN model verified using MODIS snow cover maps for the whole snow seasons and for individual month s from November to April during the period of 2002 -2008.

Month MAE (%) NSE Bias (%) R Nov.-Apr. 9.28 0.48 5.92 0.79 Nov. 4.35 0.75 1.65 0.86 Dec. 8.05 0.26 6.13 0.86 Jan. 17.05 0.18 12.25 0.61 Feb. 15.56 0.23 10.01 0.80 Mar. 11.07 0.51 6.26 0.81 Apr. 0.67 0.94 -0.01 0.71

Besides the evaluation of snow cover simulations in temporal dimension, it is also important to know the model performance in spatial dimension. Nine elevation zones were defined in the study area according to the altitudes (Figure 4.1). Table 4.2 shows the four evaluation indices of the simulated SCA at each elevation zone, and the parameters of the nine elevation zone are also demonstrated. Table 4.2 illustrates that TRAIN model showed the best performance at the medium elevations (H4-H7) according to the higher Nash-Sutcliffe efficiency scores (NSE). MAE and Bias values suggest that the overestimation errors of modeled SCA tended to increase from medium elevations to the highlands (H5-H9). It might be partly related to the higher

underestimation error of MODIS snow products in the forest covered mountain regions. In addition, a fixed threshold of 2 mm was used in this study to convert the simulated SWE to snow covered area (SCA), which was easier to be achieved at higher elevations where snow accumulated more. Therefore, the relatively smaller SWE threshold for determining snow cover extent in the mountainous regions might be another reason for the positive Bias of modeled SCA at high elevations (e.g. H7-H9). The correlation coefficient (R) at H9 elevation zone was lowest (0.62), which might be also led by the obviously lesser area (435 km2_{) compared to the other elevation zones.}

Table 4.2 Evaluation indices of mean absolute error (MAE), Nash -Sutcliffe

efficiency scores (NSE), Bias and Correlation Coefficient (R) for simulated snow covered area (SCA) by TRAIN model verified using MODIS snow cover maps for the 9 elevation zones over Rhineland-Palatinate during the period of 2002 -2008. The elevation ranges and area of the 9 elevation zones are also listed.

Elevation zones Range (m a.s.l.) Area (km2₎ MAE (%) NSE Bias (%) R H1 53-145 2527 4.70 0.42 -0.47 0.71 H2 145-214 2015 5.24 0.52 -0.48 0.76 H3 214-276 2691 6.18 0.58 0.30 0.79 H4 276-331 3136 6.75 0.63 1.86 0.79 H5 331-385 2921 7.64 0.63 3.88 0.76 H6 385-442 2630 8.81 0.62 6.55 0.72 H7 442-503 2198 10.57 0.62 9.11 0.72 H8 503-583 1318 13.55 0.55 12.78 0.73 H9 583-817 435 20.06 0.30 19.80 0.62