Heilongjiang-Amur River Basin is a transboundary basin which connects the regions of China, Mongolia, Russia and North Korea. In this basin, the cross-border region between China and Russia act as the main agricultural region of the entire area. In the context of climate change, the frequency and severity of natural disasters in internal agricultural production have increased year by year, which has a major impact on food security in the basin and even in Northeast Asia. Combining the characteristics of local natural climate conditions, the application of this knowledge characterizes and compares climatic variables such as accumulated temperature and precipitation in the region. On this basis, the common disasters such as frost and drought in the region are used for grading assessment.
By clicking and zooming, the user can intuitively and conveniently understand the data information through the map: 1) Spatial distribution and temperature division of annual growth cumulative temperature (> 0℃, > 5℃, > 10℃, > 15℃) of four temperature-sensitive crops with 0.25°×0.25° spatial resolution during 2002-2020 in the whole Heilongjiang Amur River Basin (hereinafter referred to as "the Basin"); 2) The annual total precipitation distribution in the same period and the applicability evaluation results of each product based on the station and multi-spatial scale remote sensing data; 3) Spatial distribution of the first/last frost days and frost-free periods and the results of frost disaster classification; 4) Spatial distribution of temperature vegetation drought index (TVDI) in growing season from 2007 to 2018 of the basin. The application of this knowledge has realized the sorting and archiving of relevant meteorological data in the Heilongjiang River Basin, completing the analysis and grading of the climate state and the degree of disaster impact in the past two decades, which is helpful to grasp the overall spatial law of natural disasters in agricultural production in the river basin, identify key areas where climate disasters occur, and provide data support and scientific basis for disaster prevention and crop production in this area.
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