<p>In multiple criteria decision aid (MCDA) problems, evaluating and selecting alternatives across numerous criteria often presents a significant challenge. An excessive number of criteria can induce cognitive overload in decision-makers, diminishing the reliability and validity of the outcomes. To address this complexity, organizing criteria into a hierarchical tree structure has been recommended, allowing the main problem to be decomposed into more manageable sub-problems. However, existing ranking approaches rarely offer efficient tools to handle hierarchical criteria structures. This study proposes the development of hierarchical decision support systems aimed at improving the quality of decision-making. Specifically, an extension of the well-established weighted aggregated sum product assessment (WASPAS) method, termed 2-Tuple WASPAS-H, is introduced. This new method generates partial pre-orders at each sub-level and an overall pre-order at the highest level, enabling a step-by-step analysis and interpretation of the problem. A major strength of 2-Tuple WASPAS-H lies in its capacity to produce detailed rankings at each node of the hierarchy, while also accommodating imprecise and ambiguous evaluations through a symbolic treatment of multi-granular linguistic assessments provided by groups of experts. To evaluate the relative importance of criteria across different levels, the 2-Tuple BWM is utilized. The proposed approach is validated through a real case study: the evaluation of water resources for olive tree irrigation. The analysis employs a four-level hierarchy of criteria, incorporating environmental, productive, pomological, physico-chemical, social, technological, and financial dimensions. Ultimately, a sensitivity analysis is undertaken to evaluate the stability and performance of the developed methodology.</p>