LAUSR

For large sparsely gauged basins, it is difficult to estimate long-duration probable maximum precipitation (PMP) and probable maximum flood (PMF) due to insufficient observed data and precipitation spatial distribution uncertainty. In this paper, a framework coupling the China Grid Daily Precipitation Datasets (CGDPDs) with Soil and Water Assessment Tool (SWAT) was proposed to estimate the 15-day PMP and PMF for the sparsely gauged Upper Nujiang River Basin (with a drainage area of 73,484 km2). CGDPD was tested against the observations and further corrected considering the error distribution characteristics. Results showed that 1-, 3-, 7- and 15-day maximum areal precipitations based on the corrected CGDPD were 17, 7, 4 and 18% larger than those calculated only by six observed stations’ precipitation. Then CGDPD was used as the precipitation data to estimate PMP. For the spatial distribution of PMP, the 15-day PMP process on the sub-basin scale (PMPsub-basin) could be obtained with the following procedure. First, the basin’s 15-day areal PMP was estimated. Among this estimation, the maximum 3-day PMP was estimated by moisture maximization, while the remaining 12-day PMP was estimated with the combined storm obtained by the similar process substitution method. Second, the model storm amplification approach based on water balance principle was used to distribute the areal PMP to each sub-basin to obtain the PMPsub-basin at all 27 sub-basins. The designed PMF could be finally estimated through inputting PMPsub-basin into SWAT. In comparison with PMF derived from PMP without spatial distribution, different duration PMFs could increase by 3–15% when considering PMP spatial distribution uncertainty. This study could provide a reasonable procedure to estimate long-duration PMP and PMF for similar basins.