A　hybrid　inexact　optimization　model　is　developed　for　food-water-energy　nexus　system　management　with　the　consideration　of　complex　uncertainties　and　decision　makers＇　risk　tolerance.　A　multi-stage　stochastic　fuzzy　random　programming　(MSFRP)　model　is　tailored　to　tackle　variables　with　deeper　uncertainties,　a　mixture　of　fuzzy　and　random　fuzzy　characteristics.　Allowing　to　reflect　decision　makers＇　subjective　opinion　and　risk　preference,　it　can　provide　decision　makers　the　tradeoff　information　between　system　benefit　and　risk　attitude.　The　proposed　model　was　applied　to　an　agricultural　area　Shandong　Province,　China　with　the　aim　of　maximum　total　system　benefits.　The　valuable　managerial　insights　on　optimal　cultivated　land　distribution,　water　resource　allocation,　and　energy　supply　strategies　are　provided　for　decision　makers　under　uncertainties.　Meanwhile,　the　pesticide　and　fertilizer　consumption　for　crop　planting,　and　the　carbon　emission　embodied　in　per　unit　crop　supply　are　also　quantitatively　estimated.　Moreover,　by　setting　different　water　resource　availability　scenarios,　the　impacts　of　future　water　resource　conditions　on　optimal　management　strategies　under　climate　change　are　evaluated　and　discussed.　The　results　suggested　that　rice　would　be　the　critical　crop　with　the　largest　planting　area　for　food　security　during　the　planning　horizon.　Under　scarcer　water　resource　conditions,　the　system　benefits　would　reduce　due　to　more　desalination　water　consumption　and　planting　strategy　adjustment.　However,　it　would　lead　to　less　carbon　emission　embodied　in　per　unit　crop　supply　and　relieve　local　carbon　emission　control　pressure.　Compared　to　the　conventional　multi-stage　stochastic　programming,　the　developed　MSFRP　can　be　more　effective　to　reflect　the　optimistic　and　pessimistic　attitude　of　decision　makers　and　deal　with　future　scenario　information　with　deeper　uncertainties.