In　this　study,　an　inexact　multistage　joint-probabilistic　programming　(IMJP)　method　is　developed　for　tackling　uncertainties　presented　as　interval　values　and　joint　probabilities.　IMJP　improves　upon　the　existing　multistage　programming　and　inexact　optimization　approaches,　which　can　help　examine　the　risk　of　violating　joint-probabilistic　constraints.　Moreover,　it　can　facilitate　analyses　of　policy　scenarios　that　are　associated　with　economic　penalties　when　the　promised　targets　are　violated　within　a　multistage　context.　The　developed　method　is　applied　to　a　case　study　of　water-resources　management　within　a　multi-stream,　multi-reservoir　and　multi-period　context,　where　mixed　integer　linear　programming　(MILP)　technique　is　introduced　into　the　IMJP　framework　to　facilitate　dynamic　analysis　for　decisions　of　surplus-flow　diversion.　The　results　indicate　that　reasonable　solutions　for　continuous　and　binary　variables　have　been　generated.　They　can　be　used　to　help　water　resources　managers　to　identify　desired　system　designs　against　water　shortage　and　for　flood　control,　and　to　determine　which　of　these　designs　can　most　efficiently　accomplish　optimizing　the　system　objective　under　uncertainty.