Spatiotemporal patterns of snow cover retrieved from NOAA-AVHRR LTDR: a case study in the Tibetan Plateau, China

Snow cover plays an important role in hydrological processes and seasonal water balance.Especially in the Tibetan Plateau(TP),snow cover is an important source of the Yangtze River,Yellow River and Lancang River(SRYYL),which greatly influences regional water balance.In this study,we quantified the temporal trend and spatial variation of snow cover across the TP by calibrating and developing the Advance Very High Resolution Radiometer(AVHRR)Long Term Data Record(LTDR)-derived snow cover products during 1982-2011.We also examined the relationship of snow cover with temperature and precipitation over the TP during 1982–2011.The results indicate that seasonal snow cover generally starts to accumulate from central plateau in October,while significant melting starts to occur from the southeastern plateau in May of following year.The long-term variability of snow cover is characterized by the tendency for a slight decrease in the mean snow coverage during the period of hydrological year(HY)1982–1993 and a slight increase from HY2001 to 2011,but the total snow cover area remains relatively stable over the past 30 years.The results also show that temperature plays a critical role in controlling the snow cover days.

Enhancing the soil and water assessment tool model for simulating N_(2)O emissions of three agricultural systems

Nitrous oxide(N_(2)O)is a potent greenhouse gas(GHG)contributing to global warming,with the agriculture sector as the major source of anthropogenic N_(2)O emissions due to excessive fertilizer use.There is an urgent need to enhance regional-/watershed-scale models,such as Soil and Water Assessment Tool(SWAT),to credibly simulate N_(2)O emissions to improve assessment of environmental impacts of cropping practices.Here,we integrated the DayCent model’s N_(2)O emission algorithms with the existing widely tested crop growth,hydrology,and nitrogen cycling algorithms in SWAT and evaluated this new tool for simulating N_(2)O emissions in three agricultural systems(i.e.,a continuous corn site,a switchgrass site,and a smooth brome grass site which was used as a reference site)located at the Great Lakes Bioenergy Research Center(GLBRC)scale-up fields in southwestern Michigan.These three systems represent different levels of management intensity,with corn,switchgrass,and smooth brome grass(reference site)receiving high,medium,and zero fertilizer application,respectively.Results indicate that the enhanced SWAT model with default parameterization reproduced well the relative magnitudes of N_(2)O emissions across the three sites,indicating the usefulness of the new tool(SWAT-N_(2)O)to estimate long-term N_(2)O emissions of diverse cropping systems.Notably,parameter calibration can significantly improve model simulations of seasonality of N_(2)O fluxes,and explained up to 22.5%-49.7%of the variability in field observations.Further sensitivity analysis indicates that climate change(e.g.,changes in precipitation and temperature)influences N_(2)O emissions,highlighting the importance of optimizing crop management under a changing climate in order to achieve agricultural sustainability goals.