Spatiotemporal variation of daily precipitation concentration and its potential relationship with climatic factors and land use types in the Haihe River basin, China

Abstract Precipitation concentration is a key climatic factor in the hydrologic system. The purpose of this study was to explore the spatiotemporal variation in precipitation concentration and its possible relationship with large‐scale atmospheric circulation and land use types. Based on daily precipitation recorded at 254 stations during 1961–2019 throughout the Haihe River basin (HRB), linear slope and Manner–Kendall trend analysis were used to analyse the spatial variations and trends of the annual daily precipitation concentration index (CI). The Pearson correlation coefficient and cross wavelet analysis were used to evaluate potential correlations between CI and eight climatic factors. Meteorological stations were classified into three land use types: urban type (UT), farmland type (FT), and natural type (NT) using hierarchical clustering, and the impacts of land use types on CI variations and trends were investigated. Annual CI in HRB showed a significant downward trend with a linear rate of −0.0058/decade, and about 210 of the 254 stations had a downtrend, in which 42 stations were at the significance level of 0.05 but did not reach the significance level of 0.01, and 52 stations are at the 0.01 significance level. The East Asian monsoon index (EASMI), South Asian monsoon index (SASMI), South China Sea monsoon index (SCSMI), and Southern Oscillation Index (SOI) were positively correlated with CI, whereas the Pacific Decadal Oscillation (PDO), Multivariate ENSO index (MEI), and Western Pacific index (WP) were negatively correlated. The Sunspot index (SS) had a significant resonance period of 9–14‐year with CI. EASMI and ENSO events were the dominant factors driving CI trends. The negative CI trends of UT were more significant than those of FT and NT, with linear slope gaps of −0.0041/decade (UT versus FT) and −0.0064/decade (UT vs. NT), respectively.