Land subsidence in response to declining water table at plains under sparse data is investigated using ALPRIFT, introduced recently by the authors at the stage of its proof-of-concept. ALPRIFT is a framework, which pools consensually together seven general-purpose data layers with a scoring system of prescribed rates accounting for local variations and prescribed weights accounting for their relative importance. It is a subsidence vulnerability indexing (SVI) approach, which estimates relative values and is subject to inherent subjectivities. The paper treats the transformation of SVI into a risk indexing (RI) capability through a scheme, in which ALPRIFT breaks down into ALRIF, characterising passive local effects and into water-driven PT, characterising active system-wide effects. The addition of passive and active processes renders total vulnerability but their products render a measure of risk index. A modelling strategy is formulated for SVI and RI at two levels to treat inherent subjectivities and involves data fusion by using catastrophe theory. The strategy is applied to an aquifer subject to decline in water table at the coast of Lake Urmia, with sparse data. The results provide evidence for the proof-of-concept on SVI and RI using ROC/AUC performance metrics.