A framework is formulated in this paper for data-driven modelling practices to characterise Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted information from given datasets and learning from multiple models. This can be a shift from traditional practices with the single goal of selecting a 'superior' model from multiple models without a statistical justification, which may be referred to as Exclusionary Multiple Modelling (EMM) practices. The dimensions of the framework for IMM practices are: Model Reuse (MR), Hierarchy and/or Recursion (HR), a provision of 'Elastic' model-Learning Environment (LE) and Goal-Orientation (GO) - leading to the acronym of RHEO. Proof-of-concept is presented for IMM-RHEO using three testcases: the Caspian Sea (19-years of data), Lake Urmia (50-years of data) and Lake Van (73-years of data), approx. 500 km apart. IMM practices are implemented by investigating four strategies for each testcase. The learning from the results includes: (i) the IMM strategies are capable of enhancing the accuracy of predicted water levels; (ii) the accuracy of predicting the sea-state of the Caspian Sea serves confidence building on accuracy; and (iii) the time-length of the record of Lake Van is long enough for the confidence building on the study of possible trends. IMM serves a bottom-up learning opportunity for Lake Urmia that its distressed state is due to being deprived of compensation flows without contributions from climate change. Arguably, a good management policy is the key for its restoration. IMM is at its infancy but arguably, its potential application areas are wide.