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ABSTRACT Parameterisation of fully coupled integrated hydrological models is challenging. The state‐of‐the‐art hydrogeology models rely on solutions of coupled surface and subsurface partial differential equations. Calibration of these models with traditional optimisation methods are not yet viable due to the high computational costs. Prior knowledge of the range of the parameters can be helpful as a starting point, however, due to natural variations, abstractions and conceptualizations used in modelling, a systematic exploration of the variable space is needed. In this study, we utilise the natural clustering of the soils based on their saturated and unsaturated hydraulic behaviour derived from soil texture maps in conjunction with two level Latin hypercube sampling to effectively explore model parameter spaces. Soil texture maps are similar to USDA soil classifications; however, the objective is to classify the soil based on their unsaturated behaviour, rather than soil texture. The method has never been utilised in the modelling and the results show that it can be applied to larger watersheds. The area of study is Hubbard Brook Experimental Forest, a northern hardwood forest in the White Mountains of New Hampshire, USA. An average Nash–Sutcliffe value of 0.80 is achieved for hourly discharge for the eight streams in the catchment. The Nash–Sutcliffe measure shows a 7% improvement with the addition of the snow melt and evapotranspiration parameters in the second stage. Exchange flux patterns vary seasonally in the catchment with largest infiltration occurring in spring.