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【Objective】 Study the characteristics of land use change in Huaihe River basin and predict the land use change in Huaihe River basin in 2030, in order to achieve reasonable development and utilization of land resources in the basin. 【Method】 The land use transfer matrix and land use dynamic were used to analyze the land use change characteristics based on the land use data of Huaihe River basin from 1990 to 2020. The land use pattern of Huaihe River basin in 2010 and 2015 were simulated based on the cellular automata-Markov (CA-Markov) model, and the land use change trend of the basin in 2030 is predicted under the condition of meeting a certain degree of accuracy. 【Result】 ①Cultivated land and construction land area account for more than 80% of the land use area, which were the two most dominant land use types in Huaihe River basin; ②The decrease of cultivated land and the continuous expansion of construction land were the most obvious change characteristics of land use in Huaihe River basin in the past 30 years; ③The land use in 2010 and 2015 simulated by the CA-Markov model based on the Kappa coefficients of 2010 and 2015 simulated based on CA-Markov model are 0.937 and 0.944, respectively, with high simulation accuracy; ④The predicted land use changes in Huaihe River basin in 2030 showed that the reduction of arable land and the expansion of land for construction are still the main trends, but the magnitude of the changes in both of them is slowing down, and the changes in forest land and grassland are not significant, but the area of watershed continues to increase. 【Conclusion】 The substantial expansion of construction land and the continuous decrease of arable land in Huaihe River basin should be emphasized, and the land use change in 2030 simulated based on CA-Markov model can provide a reference for the future land use development of the basin.

【Objective】 Statistical characteristics and frequency of runoff is crucial for water resources planning and water resource project construction, especially when runoff in watersheds shows significant variation due to environmental changes. Taking upper reach of the Heihe River basin as an example, this paper examines the impact of runoff variation on statistical characteristics and frequency of the runoff analysis. 【Method】 Annual runoff and annual maximum flooding flow measured from the reach were used in our study. Two types of analysis windows, the fixed-point variable-width window (Type A window) and the variable-point fixed-width window (Type B window), were used to extract the runoff sequences (window sequences) for analysis. The P-Ш type curves were utilized to analyze the statistical characteristics of the window series, from which we calculated the runoff design values. The trends and discontinuity in the window series were identified using the linear trend analysis, MMK trend test, and heuristic segmentation method. 【Result】 ① The estimated annual runoff parameters (Ex) showed a significant increasing trend and abrupt changes in both Type A and Type B window analyses. The coefficient of variation (Cv) showed a significant increasing trend in Type A window analysis and abrupt changes in Type B window analysis. The coefficient of skewness (Cs) showed abrupt changes in Type B window analysis. Design values for different frequencies of the annual runoff showed a significant increasing trend and abrupt changes in both types of windows. ② The estimated parameters in the annual maximum showed abrupt changes in Type A window analysis, while the Ex showed abrupt changes in Type B window analysis. Cs showed a significant increasing trend and abrupt changes in Type B window analysis. Design values for different frequencies of the annual maximum flooding did not identify any significant increasing trend in Type A window analysis but showed sudden changes. In Type B window analysis, there was a significant increasing trend and abrupt changes. 【Conclusion】 Analyzing runoff processes should use long sequences to capture the whole spectrum of the runoff pattern. Using long runoff series can mitigate the influence of extreme runoffs on statistical characteristics and frequency of runoff analysis.

【Objective】 Various methods have been proposed to classify changes in runoff in catchments, but how to assess their reliability remains a challenge. In this paper, we present a method to assess the reliability of the annual runoff classification for wet and dry years calculated by different methods. Its effectiveness was tested against data measured from a watershed. 【Method】 The reliability of the methods for classifying annual runoff for wet and dry years is analyzed based on their stability and predictability. The assessment is based on the cross-validation method and Markov chain method. We evaluate the stability and predictability of the classified results obtained by the mean-standard deviation method (MSD), gray relational analysis (GRA), and set-pair analysis (SPA). The difference in the classification and the transfer probability of the indices is established to evaluate the stability and predictability of the classified results. The proposed model is tested against annual runoff measured from 1956—2021 at the Tangnaihai Hydrological Station in the upper reaches of the Yellow River basin. 【Result】 ①Analysis using the cross-validation method and Markov chain showed that the results calculated by different classification methods vary, indicating that the stability and predictability of different methods are different. ②The classification difference index indicates that the GRA method is most stable and the MSD method is least stable. The transfer probability differences indicates that the GRA method has the best predictability and the MSD has the worst. ③Considering stability and predictability, the GRA method is most reliable for classifying annual runoff abundance and depletion, and the MSD method is the least. 【Conclusion】 The reliability of different methods for classifying annual runoff for wet and dry years varies for the same watershed. The method we developed from the cross-validation method and Markov chain can effectively assess the reliability of the results calculated by different classification methods.