Sensitivity analysis of model parameters and inputs is an important research method to improve model accuracy and calibration efficiency. The purpose of this paper is to explore the sensitivity of model parameters and model inputs in arid areas, and then to study the linear or nonlinear relationship of model sensitivity taking Erjiama small watershed in Jungar banner, Ordos, Inner Mongolia, China as the research object. Based on the calibration of CASC2D-SED model, four model parameters, including hydraulic conductivity, Manning coefficient, Suction head and vegetation interception, and one model input-river network were perturbed at a variation rate of 25%. The hydrological process is simulated by using the combination of 20 model parameters and model inputs after perturbation, and then the sensitivity analysis of model parameters and inputs is studied. The results show that: 1) The sensitivity of Manning coefficient to peak discharge and peak arrival time is non-linear, while the sensitivity of hydraulic conductivity, Suction head and vegetation interception to peak discharge, peak arrival time, simulated total discharge and infiltration amount is linear; 2) With the increase of hydraulic conductivity, Suction head and vegetation interception, the peak discharge and simulated total discharge decrease gradually, while the peak arrival time and infiltration amount increase gradually; 3) With the increase of Manning coefficient, the simulated total flow decreases gradually, while the infiltration rate increases gradually; 4) With the increase of the number of tributaries, the peak discharge, the simulated total discharge and the total amount of infiltration gradually decrease, while the flood peak arrival time presents a U-shaped change; 5) With the increase of Manning coefficient, hydraulic conductivity, Interception and Suction head, the simulated sediment flow decreases. Manning coefficient is more sensitive to the simulated amount of clay than the other three model parameters. Sensitivity analysis of parameters and inputs of CASC2D-SED model plays a guiding role in model

The size of the texture extraction window impacts image tree species classification, and the determination of the optimal texture extraction window requires the supervision of a specific classifier for accuracy. Therefore, it is necessary to analyse which kind of classifier is more suitable and should be to choose. In this study, we extracted eight types of textures, namely mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation, changed the window size by gradient increase and used maximum likelihood classification (MLC) and random forest (RF) to supervise and determine their optimal extraction windows, respectively. Finally, the optimised time consumption and classification accuracy for tree species classification was identified. The time consumption of MLC was significantly less than that of RF; however, neither was very long; for most textures, the optimal texture extraction window determined by MLC supervision was larger than that determined by RF supervision; in the classification of most feature sets, the overall accuracy obtained by MLC was less than that of RF. Because the time consumption of the texture extraction was much greater than that of the image classification, the comprehensive trade-off indicates that using RF supervision to determine the optimal window for texture extraction was more conducive to tree species recognition.