| dc.description.abstract | Mountainous snow is essential substance in the earth surface because it is important for inland fresh water provision, global and local climate regulation and water cycles. The research is to monitor mountainous snow cover, simulate snow melt runoff using snowmelt runoff model (SRM) and assimilate state from remote sensing data into snow melt runoff model in the Langtang catchment.
The first topic is to monitor snow cover in the Langtang catchment using MODIS snow product. It gives an analysis of snow cover area change with time. Inter- and between- annual analysis methods are two different perspectives for snow cover time monitoring. Some interesting trends would be drawn in this section. The results showed a seasonal variation in snow cover with two peaks and one minimum in snow cover percentage trends. The maximum snow cover area is about 1800 km2 (94% in the catchment) in winter time, while in summer time, the bottom is only about 200 km2 (10% in the catchment). The reason of this seasonal variation of snow is explained by temporal variation in local temperature and precipitation. In summer time, the temperature is above 0 celsius and snow metling occurs. During winter time with quite low temperature, the precipitation is mainly in the form of snow so the snow cover area expands graduately. But in many winter time, there is a drought period without preciptation, which causes the snow cover to reduce. This leads to a second snow cover area percentage peak. After the dry period, snow recession may end, snow cover increases again,which leads to peak area.
The second topic focused on the snowmetl runoff modeling. Snowmelt Runoff Model (SRM) has been applied into the Langtang Khola catchment to model snowmelt runoff and discharge. It simulates snow melt, snowmelt runoff and discharge. Remote sensing data provide important snow cover information for the snow storage. The remote sensing snow cover data have been integrated into SRM model to update ice storage maps for every eight days. The ice storage updates in SRM model are from the estimation from ice flux budget. Integration remote sensing data with RSM model can improve model performance.
The final component is to quantify model uncertainties and narrow model errors using data assimilation techniques. Ice storage is an important state for SRM model. Icestorage maps are simulated from remote sensing snow cover data. In data assimilation processes, ice storage maps from remote sensing are used to update model state. Monte Carlo simulation, ensemble Kalman filter and particle have been applied into the Snowmelt Runoff Model (SRM) to quantify model errors. For a low number of ensemble/ particle members, EnKF performed better than particle filter. For an intermediate number of ensemble/ particle member, there is little difference of the performances between the two assimilation techniques. But for large number of the ensemble/ particle members, particle filter outperformed ensemble Kalman filter. While increase in the number of ensembles did not improve the performance in ensemble Kalman filter.
The study has covered a wide range for snow research. Snow cover is the basis for snowmelt runoff modeling and snowmelt runoff model is the basis for assimilation. The remote sensing snow cover data are integrated and assimilated into snowmelt runoff model. | |
