Hydrologic　basin　models　are　generalizations　of　natural　hydrologic　processes　and　inevitably　contain　multiple　uncertainties　in　the　simulation　of　rainfall-runoff　events.　These　uncertainties　typically　include　input　uncertainty,　model　structure　uncertainty,　and　model　parameter　uncertainty.　The　input　uncertainty　is　divided　into　rainfall　calculation　uncertainty　(RCU)　and　precipitation　forecasting　uncertainty.　For　model　parameter　uncertainty,　there　are　only　a　few　parameters　in　a　hydrologic　basin　model　that　make　significant　contributions　to　flood　forecasting　so　that　only　the　probability　distribution　functions　of　those　sensitive　parameters　need　to　be　evaluated.　In　this　study,　a　new　method　of　RCU　assessment　is　derived　using　an　inverse　sampling　gauge　(ISG)　approach,　in　which　the　influencing　factors　of　the　RCU　are　addressed　in　the　calculated　area　precipitation　and　standard　deviation.　Additionally,　the　coefficient　of　variation-Nash-Sutcliffe　efficiency　measure　(CV-NS)　method　is　introduced　to　identify　the　sensitive　parameters　of　a　hydrologic　model.　The　methods　of　ISG　and　CV-NS　are　tested　in　Huangnizhuang　Basin,　China,　in　the　evaluation　of　the　RCU　and　the　parameters　uncertainty　of　the　Xinanjiang　model.　It　is　indicated　that　the　ISG　method　is　useful　in　RCU　estimation　by　deriving　the　conditional　probabilistic　distribution　of　areal　precipitation,　and　the　CV-NS　method　is　effective　and　simply　in　the　operation　of　sensitive　parameters＇　identification.　In　addition,　the　continuous　ranked　probability　skill　score　(CRPSS)　is　employed　as　an　indicator　to　show　the　relative　performance　of　predictions.　Three　probabilistic　predictions　considering　different　sources　of　uncertainties　are　conducted　and　compared　with　the　deterministic　prediction.　The　results　of　the　comparisons　suggest　that　predictions　considering　one　or　more　uncertainties　have　higher　predictive　performance　than　the　deterministic　one.　Moreover,　main　source　of　uncertainty　can　be　identified　by　the　results　of　CRPSS　among　different　probabilistic　predictions.　In　this　study　area,　the　model　parameters＇　uncertainty　is　the　main　uncertainty.　©　2019　American　Society　of　Civil　Engineers.