In　this　contribution,　the　authors　present　their　preliminary　investigations　into　modeling　the　rainfall-runoff　generation　relation　in　a　large　subtropical　catchment　(Jiulong　River　catchment)　on　the　southeast　coast　of　China.　Previous　studies　have　mostly　focused　on　modeling　the　streamflow　and　water　quality　of　its　small　rural　subcatchments.　However,　daily　runoff　on　the　scale　of　the　whole　catchment　has　not　been　modeled　before,　and　hourly　runoff　data　are　desirable　for　some　oceanographic　applications.　Three　methods　are　proposed　for　modeling　streamflow　using　rainfall　outputted　by　the　Weather　Research　Forecast　(WRF)　model,　calculated　potential　evaporation　(PET),　and　land　cover　type:　(i)　a　ridge　regression　model;　(ii)　NPRED-KNN:　a　nonparametric　k-nearest　neighbor　model　(KNN)　employing　a　parameter　selection　method　(NPRED)　based　on　partial　information　coefficient;　(iii)　the　Hydrological　Simulation　Program-Fortran　(HSPF)　model　with　an　hourly　time　step.　Results　show　that　the　NPRED-KNN　approach　is　the　most　unsuitable　method　of　those　tested.　The　HSPF　model　was　manually　calibrated,　and　ridge　regression　performs　no　worse　than　HSPF　based　on　daily　verification,　whilst　HSPF　can　produce　realist　daily　flow　time　series,　of　which　ridge　regression　is　incapable.　The　HSPF　model　seems　less　prone　to　systematic　underprediction　when　replicating　monthly-annual　water　balance,　and　it　successfully　replicates　the　baseflow　index　(the　flow　intensity)　of　the　Jiulong　River　catchment　system.