Spatiotemporal Complexity Analysis of Daily Precipitation in a Changing Environment in Heilongjiang Province, China

摘要

Complexity analysis is an important tool for research on water resources. Although physical methods and quantitative detection are typically used to explore system complexity, this study uses a method combining probabilistic statistics with nonlinear science. The precipitation concentration index (CI), Mann-Kendall trend testing, probability density functions, and chaotic characteristic quantity are used to assess the spatiotemporal complexity of precipitation in Heilongjiang Province, China. The results show that the annual precipitation in Heilongjiang Province exhibits distinct spatial patterns; the highest values occurred at 128 degrees E, gradually diminishing toward the east and west. Maximum precipitation was recorded at Shangzhi station (646mm). A negative value of Z, an indicator of a precipitation trend, indicates a decreasing trend and a positive Z value indicates an increasing trend. In Yichun and Shangzhi, areas with greater precipitation, Z values were -1.21 and -4.88, respectively. In contrast, Z values were 3.02 and 0.93 in Qiqihar and Tailai, respectively, areas with lower precipitation. These findings indicate that precipitation differences between different regions have decreased, with the distribution becoming more uniform. Using ArcGIS to determine the chaotic characteristics of daily precipitation, values in the central and northeastern plains were high, whereas those in northwestern mountains were low. Maximum max values (3.456x10-3), an indicator of the degree of chaos, were obtained at the Yilan and Suifenhe stations, whereas the minimum value (9x10-5) was obtained at the Huma station. The authors analyze factors that influence the hydrological cycle in the study region by combining regional population and natural resource conditions. The results of this research will provide a reference for existing research and a theoretical basis for the scientific management of water resources in the region.