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The national water resources monitoring capacity building project which started in 2012 in China is an important way to improve the level of water conservancy information. It requires that the historical time-series monitoring data of water resources should be complete and reliable so that it can be used to support data analysis and decision making. The basic scenarios for monitoring abnormal data were summed up and a comprehensive model was proposed, aiming at abnormal data detection and recovery. Moving average fitting and ensemble empirical mode decomposition (EEMD) method were introduced to identify both observable and non-observable abnormal monitoring data. The particle swarm optimization based least squares support vector machine (PSO-LSSVM) was then adopted for abnormal data recovery and imputation. All above methods were tested with the daily water consumption monitoring data of water company. Results showed that the feature vector that contained exception data could be well preserved by moving average fitting and EEDM method and the effective reconstruction of water monitoring data was achieved, exhibiting better applicability than traditional statistical methods. Moreover, it can be observed that the PSO-LSSVM model had the ability to further improve the fitting results of the time-series data that excluded outliers. The fitted curve conformed to the seasonal fluctuation rule and it was consistent with the actual state of water demand. Accordingly, the objective of recovering the excluded data exception could be achieved reasonably by using this method. Furthermore, these methods can be applied to the analysis of monitoring data in other areas.