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: NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution (1.25˚ 1˚) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km 3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. ), FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

Long-term, high spatial and temporal resolution atmospheric and hydrologic data are crucial for water resource management. However, reliable high-quality precipitation and hydrologic data are not available in various regions around the world. This is, in particular, the case in transboundary regions, which have no formal data sharing agreement among countries. This study introduces an approach to construct long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region by the coupled physical hydroclimate WEHY-WRF model. For the case study, Da and Thao River watersheds (D-TRW), within Vietnam and China, were selected. The WEHY-WRF model was set up over the target region based on ERA-20C reanalysis data and was calibrated based on existing ground observation data. After successfully configuring, WEHY-WRF is able to produce hourly atmospheric and hydrologic conditions at fine resolution over the target watersheds during 1900–2010. From the modeled 72 h precipitation and flood events, it can be seen that the main precipitation mechanism of DRW and TRW are both the summer monsoon and tropical cyclone. In addition, it can be concluded that heavy precipitation may not be the only reason to create an extreme flood event. The effects of topography, soil, and land use/cover also need to be considered in such nonlinear atmospheric and hydrologic processes. Last but not least, the long-term high-resolution extreme 72 h precipitation and hillslope flood maps over a tropical transboundary region, D-TRW, were constructed based on 111 largest annual historical events during 1900–2010.