Abstract: Sensitivity analysis of parameters, calibration and validation of eco-hydrological models are essential for model evaluation and application. It is important in model application to accurately estimate the values of model parameters and to further improve model prediction capacity. Based on eco-hydrological process, the Laio soil moisture dynamics stochastic model (Laio model) was used to describe daily water balance in active soil depth of ridge-furrow rainwater harvest system during growing season to analyze the effects of the interactions among plants, soil and environment under different climatic conditions on soil water balance and plant water conditions. The performance of the Laio model varied with climatic zone due to the heterogeneity of climate, vegetation and soil characteristics. In this study, in order to establish an effective system for parameter sensitivity analysis, calibration and validation of the Laio model in a ridge-furrow rainwater harvesting system in a semi-arid area, a field experiment with a randomized complete block design was conducted during the 2012 and 2013 oat growing seasons at Dingxi Arid Meteorology and Ecological Environment Experimental Station. The experiment was designed to investigate the parameter sensitivity and to determine the optimal mode of parameter optimization of the Laio model under various mulching materials (common plastic film, biodegradable film mulch and manually compacted soil) and various ridge-furrow ratios (60 cm:30 cm, 60 cm:45 cm and 60 cm:60 cm). The methods included multi-factor sensitivity analysis, simplex method (ISM), particle swarm optimization algorithm (PSO) and hybrid particle swarm optimization algorithm (HPSO). Also continuously monitored soil moisture, precipitation runoff and daily precipitation data for 2012-2013 were used to run the model. The results indicated that:(1) mean precipitation per rainfall event (α) and soil saturation degree at wilting point (s_w) were the most sensitive parameters for probabilistic density function of soil moisturep(s) in different experimental treatments. While the sensitivity of p(s) to α was more obvious under low soil moisture content, that to s_w was more obvious under high soil moisture content. (2) There were good agreements among the results of modelling using optimized parameters of the Laio model for the three optimization algorithms (ISM, PSO and HPSO) and the observation values, which were determined from the p(s) curve. This included curve peak value (CPV), curve peak position (PP), 95% confidence interval (CI95%) and consistency measure (CM). All of these indicated that the optimized parameters of the Laio model using the ISM, PSO and HPSO methods correctly estimated p(s) of ridge-furrow rainwater harvesting. (3) The HPSO method not only improved global optimization performance, but also quickened convergence and gave robust results with good quality. It was an effective optimization method for the Laio model calibration and validation. The study improved the efficiency of model parameter calibration, upgraded the accuracy of model simulation results and provided guidance for application of the Laio model in ridge-furrow rainwater harvesting research.