For　flood　simulation　in　small-and　medium-sized　catchments,　discharge　observations　may　be　used　to　update　model　states　of　a　distributed　hydrological　model　to　improve　performance.　The　ensemble　Kalman　filter　(EnKF)　has　been　widely　used　for　hydrological　assimilation　due　to　its　relative　simplicity　and　robustness.　An　advantage　of　the　EnKF　is　that　it　is　easy　to　include　different　sources　of　uncertainty,　therefore　the　choice　of　error　model　is　crucial　for　the　application　of　the　EnKF　assimilation.　This　paper　describes　an　EnKF　assimilation　scheme　for　estimating　error　models　using　the　maximum　a　posteriori　estimation　method　(MAP).　We　test　this　scheme　in　two　small　and　medium-sized　catchments　in　China　with　different　characteristics,　and　in　addition　compared　the　performance　differences　under　two　kinds　of　rainfall　forcing.　We　show　that　MAP　is　beneficial　in　specifying　error　models　and　providing　reliable　ensemble　spread.　The　assimilation　scheme　can　effectively　ameliorate　the　degradation　of　distributed　hydrological　model　performance　due　to　uncalibrated　model　parameters　and/or　poor　quality　of　input　data.