Interpolating precipitation data is of prime importance to hydrological design, modeling, and water resource management. Various models have been developed that estimate spatial precipitation patterns. The purpose of this study is to analyze different precipitation interpolation schemes at different time scales in order to improve the accuracy of discharge simulations. The study was carried out in the upstream area of the Changjiang River basin. The performance of all selected methods was assessed using cross-validation schemes, with the mixed methods ultimately displaying the best performance at all three time scales. However, the differences in performance between the spatial interpolation methods decreased with increasing time scales. The unifying catchment Soil and Water Assessment Tool (SWAT), ‘abcd’, and the Budyko equation were employed at the daily, monthly, and annual scales, respectively, to simulate discharge. The performance of the discharge simulation at the monthly and annual time scales was consistent with their ranks of spatial precipitation estimation. For coarse, or long period, precipitation, there were no significant differences. However, the mixed methods performed better than the single model for the daily, or short, time scale with respect to the accuracy of the discharge simulation.

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Long-term scheduling and short-term decision-making for water resources management often require understanding the relationship of water yield pattern between the annual and monthly scales. As the water yield pattern mainly depends on land cover/use and climate, a unifying catchment water balance model with factors has been adopted to derive a theoretical water yield pattern with annual and monthly scales. Two critical values at the parameters ε=1-√2/2 and ϕ=1.0 are identified. The parameter ε referring to the water storage (land use/cover) and evaporation (climate) changes can make more contribution than ϕ for water yield when ϕ>1.0, especially with ε<1-√2/2. But there is less contribution made by ε when ϕ<1.0. The derived theoretical water yield patterns have also been validated by the observed data or the simulated data through the hydrological model. Due to the bias of the soil moisture data, a lot of the estimated parameter ε values are over its theoretical range, especially for the monthly scale in humid basins. The performance of the derived theoretical water yield pattern at annual scale is much better than that at monthly scale while there are only a few data sets from the arid basin at every months fall within their theoretical ranges. Even the relative contributions of ε is found to be bigger than those of ϕ due to ε<1-√2/2 and ϕ>1.0, there are no significant linear relationships between annual and monthly parameters ε and ϕ. Our results not only validate the derived theoretical water yield pattern with the estimated parameter directly by the observed or simulated data rather than the calibrated parameter, but also can guide for further understanding physical of water balance to conversion time scales for the combing long-term and short-term water resources management.