The hydrological data fed to hydrological decision support systems might be untimely, incomplete, inconsistent or illogical due to network congestion, low performance of servers, instrument failures, human errors, etc. It is imperative to assess, monitor and even control the quality of hydrological data residing in or acquired from each link of a hydrological data supply chain. However, the traditional quality management of hydrological data has focused mainly on intrinsic quality problems, such as outlier detection, nullity interpolation, consistency, completeness, etc., and could not be used to assess the quality of application – that is, consumed data in the form of data supply chain and with a granularity of tasks. To achieve these objectives, we first present a methodology to derive quality dimensions from hydrological information system by questionnaire and show the cognitive differences in quality dimension importance, then analyze the correlations between the tasks, classify them into five categories and construct the quality assessment model with time limits in the data supply chain. Exploratory experiments suggest the assessment system can provide data quality (DQ) indicators to DQ assessors, and enable authorized consumers to monitor and even control the quality of data used in an application with a granularity of tasks.