Abstract
Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai–Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai–Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai–Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas.
Highlights
Information about the spatial distribution of precipitation is vital for the research and applications of fields such as meteorology, hydrology, agriculture, ecology, and environmental science [1,2,3].In normal cases, the precipitation distribution is mainly determined by the large-scale atmospheric circulation; for regions with complex terrain, the terrain can render precipitation distribution rules by affecting the large-scale weather system, atmospheric airflow, and microphysical processes of clouds [4,5,6]
The study first studied the relationship between the seasonal and annual mean precipitation distribution and the 11 geographical or topographical factors on the northeast slope of the Qinghai–Tibet Plateau, and created multivariate linear regression precipitation estimating models based on the studied relationship
A residual error correction method was employed to improve the overall performance of the estimation models
Summary
Information about the spatial distribution of precipitation is vital for the research and applications of fields such as meteorology, hydrology, agriculture, ecology, and environmental science [1,2,3].In normal cases, the precipitation distribution is mainly determined by the large-scale atmospheric circulation; for regions with complex terrain, the terrain can render precipitation distribution rules by affecting the large-scale weather system, atmospheric airflow, and microphysical processes of clouds [4,5,6]. Interpolation technology based on geographical and topographical impact factors has the potential of turning into an important means of precipitation distribution estimation. Many experts and scholars have set out to study the impact of geographical and topographical factors on the precipitation distribution in different regions or areas, and established a variety of precipitation distribution estimating methods. The geographical and topographical factors that influence the precipitation distribution in different areas, including the longitude, latitude [10], station elevation [9], maximum height of mountains facing different directions [11], average elevation of the station’s vicinity [12], slope, exposure, average slope and exposure in the vicinity of the station, distance from vapor source, distance from ridge [10], and landform type [13]. The precipitation estimating modes that have been established based on the aforesaid factors cover artificial neural network (ANN) [10], partial least squares regression (PLS) [14], and MLR (multivariate linear regression) [14], in which
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