This study presents an application of a multiple point geostatistics (MPS) to map landforms. MPS uses information at multiple cell locations including morphometric attributes at a target mapping cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform. The technique requires a training data set, consisting of a field map of landforms and a DEM. Mapping landforms proceeds in two main steps. First, the number of cells per landform class, associated with a set of observed attributes discretized into classes (e.g. slope class), is retrieved from the training image and stored in a frequency tree, which is a hierarchical database. Second, the algorithm visits the non-mapped cells and assigns to these a realization of a landform class, based on the probability function of landforms conditioned to the observed attributes as retrieved from the frequency tree. The approach was tested using a data set for the Buëch catchment in the French Alps. We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood. The training data set was taken from multiple locations, covering 10% of the total area. The mapping was performed in a stochastic framework, in which 35 map realizations were generated and used to derive the probabilistic map of landforms. Based on this configuration, the technique yielded a map with 51.2% of correct cells, evaluated against the field map of landforms. The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas. Debris slope was mapped with the highest accuracy, while MPS shows a low capability in mapping hogback and glacis. The mapping accuracy is highest for training areas with a size of 7.5-10% of the total area. Reducing the size of the training images resulted in a decreased mapping quality, as the frequency database only represents local characteristics of landforms that are not representative for the remaining area. MPS outperforms a rulebased technique that only uses the morphometric attributes at the target mapping cell in the classification (i.e. one-point statistics technique), by 15% of cell accuracy.

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Despite the increasing role of models in hydrological research and decision-making processes, only few accounts of the nature and function of models exist in hydrology. Earlier considerations have traditionally been conducted while making a clear distinction between physically-based and conceptual models. A new philosophical account, primarily based on the fields of physics and economics, transcends classes of models and scientific disciplines by considering models as "mediators" between theory and observations. The core of this approach lies in identifying models as (1) being only partially dependent on theory and observations, (2) integrating non-deductive elements in their construction, and (3) carrying the role of instruments of scientific enquiry about both theory and the world. The applicability of this approach to hydrology is evaluated in the present article. Three widely used hydrological models, each showing a different degree of apparent physicality, are confronted to the main characteristics of the "mediating models" concept. We argue that irrespective of their kind, hydrological models depend on both theory and observations, rather than merely on one of these two domains. Their construction is additionally involving a large number of miscellaneous, external ingredients, such as past experiences, model objectives, knowledge and preferences of the modeller, as well as hardware and software resources. We show that hydrological models convey the role of instruments in scientific practice by mediating between theory and the world. It results from these considerations that the traditional distinction between physically-based and conceptual models is necessarily too simplistic and refers at best to the stage at which theory and observations are steering model construction. The large variety of ingredients involved in model construction would deserve closer attention, for being rarely explicitly presented in peer-reviewed literature. We believe that devoting more importance to identifying and communicating on the many factors involved in model development might increase transparency of model building

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