Outlier　detection　is　one　of　the　major　branch　in　data　mining　which　has　been　applied　in　different　fields.　Researchers　have　focused　on　the　outlier　detection　in　time　series,　but　rarely　spatial　series.　In　this　paper,　we　propose　a　new　outlier　detection　method　based　on　k-nearest　neighbour　(KNN)　and　Mahalanobis　distance,　which　is　first　applied　to　the　water　field.　Experimental　results　verify　that　the　algorithm　has　good　accuracy　and　effectiveness　in　outlier　detection　for　water　quality　spatial　series　dataset.