Change Characteristics of Rainfall Erosivity in the Black Soil Region in Northeast China from 1961 to 2020

Rainfall erosivity plays a crucial role in assessing and forecasting soil erosion. Accurate assessment of rainfall erosivity and understanding its spatiotemporal distribution patterns can provide valuable insights for protecting black soil in Northeast China. In this study, we utilized daily rainfall observation data from 142 meteorological stations spanning the years 1961 to 2020 and one-hour precipitation data from 20 stations for 2019 and 2020 in the Black Soil Region in Northeast China (NEBS). We calculated rainfall erosivity using both the classical algorithm and a daily rainfall model, verifying the accuracy of daily rainfall data in calculating erosivity. We then conducted a comprehensive analysis of the spatiotemporal characteristics of rainfall erosivity in NEBS and its constituent provinces and districts, employing trend analysis, wavelet analysis, and IDW (Inverse Distance Weighted) spatial interpolation. Our findings indicate that the semi-monthly model is suitable for calculating rainfall erosivity in NEBS. The correlation coefficients between rainfall erosivity calculated using the classical algorithm and that estimated using the semi-monthly model were 0.9804 and 0.9894 for the years 2019 and 2020, respectively. The intra-annual distribution curves of rainfall erosivity in NEBS and each province or district displayed a single peak, with a prominent peak occurring in the latter half of July. Notably, the total proportion of erosivity generated by rainfall in July and August ranged from 65.4 to 67.7%, underscoring a high degree of concentration. Over the period from 1961 to 2020, the spatial distribution of annual rainfall erosivity (referred to as \({{R}_{a}}\) ) in NEBS exhibited a general decreasing trend from south to northwest and northeast. Temporal changes showed an overall non-significant fluctuating upward trend, with \({{R}_{a}}\) at 81.7% of the stations displaying an upward trajectory, and 32.8% of these stations showing a significant increase (P < 0.1). The temporal variation characteristics of each province and region were as follows: \({{R}_{a}}\) showed a significant upward trend (P < 0.05) in Heilongjiang, while the increase and decrease trends in the other three provinces or regions were less pronounced. Notably, the \({{R}_{a}}\) in NEBS and each province or region from 1961 to 2020 exhibited a significant cycle of 3–4 years on a smaller scale.