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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 Buech 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 rule-based 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.

Hydrological forecasting and predictions under environmental change are often hampered by a lack of historical flow measurements and catchment physical data to characterize the system’s behaviour. This thesis presents a parsimonious semi-distributed rainfall-runoff modelling framework based on hydrological response units (HRUs) in which local-scale observable catchment characteristics can be directly used to parameterize the closure relations at the HRU scale. Thus, the modelling framework can potentially be used in ungauged basins where there is no sufficient data for ad-hoc parameter identification (i.e. calibration). The first part of thesis focuses on the development of Hortonian runoff closure relation for HRUs. An extensive data set of rainfall-runoff responses from hypothetical HRUs (6 x105 scenarios), generated from a detailed physically-based hydrological model, is used as a surrogate of real-world data sets to identify the form and parameters of the closure relation. These parameters are, in turn, related to local-scale HRU observables and HRU geometry for each scenario run, resulting in the parameter library to be used for the estimation of closure relation’s parameters for the HRUs outside the synthetic data sets. The closure relations show satisfactory performance in reproducing the observed hydrographs in the 16-km2 catchment in French Alps. Calibration of the closure relation against the observed discharge results in the saturated hydraulic conductivity that is comparable to the values obtained from plot measurements in the study catchment. Thus, the closure relation may be used without calibration if the local-scale observable HRU properties are correctly estimated. The second part of thesis investigates a technique for automated HRU delineation to support a model application at a large scale. This is done using a multiple-point geostatistics (MPS) technique in the context of geomorphological mapping. The MPS technique uses training images to derive the conditional relationships between occurrences of geomorphological types (HRUs) and a set of terrain attributes, consisting of four local morphometric properties and surrounding landforms at two locations. These relations are stored in a frequency database. In the mapping stage, a realization of a geomorphological class is assigned to the target mapping cell based on the probability function of landform class occurrences conditioned to the observed attributes, as retrieved from the frequency database. This technique is tested over 280 km2 in the Buëch catchment, France, using different sizes of training images. The best mapping accuracy (i.e. 51.2% of correct cell, evaluated against the field geomorphological map) can be obtained using training images covering 10% of total area. Small geomorphological features, i.e. hogbacks and glacises, are underrepresented in the automated maps due to sampling bias of these units in the training images. Using these automated geomorphological maps as the HRUs, thus, leads to substantial discharge underestimation, particularly in the dry period where hogbacks are the main runoff-contributing areas. However, this error has small effects for the predictions in the wet period because the catchment runoff is generated from many HRUs. The modelling framework presented in this thesis shows a promise and could serve be as a blueprint for predictions in ungauged basins.

This paper presents an evaluation of the closure relation for Hortonian runoff, proposed in Vannametee et al. (2012), that incorporates a scaling component to explicitly account for the process heterogeneity and scale effects in runoff generation for the real-world case studies. We applied the closure relation, which was embedded in an event-based lumped rainfall–runoff model, to a 15 km2 catchment in the French Alps. The catchment was disaggregated into a number of landform units, referred to as Geomorphologic Response Units (GRUs), to each of which the closure relation was applied. The scaling component in the closure relation was identified using the empirical relations between rainstorm characteristics, geometry, and local-scale measurable properties of the GRUs. Evaluation of the closure relation performance against the observed discharge shows that the hydrograph and discharge volume were quite satisfactorily simulated even without calibration. Performance of the closure relation can be mainly attributed to the use of scaling component, as it is shown that our closure relation outperforms a benchmark closure relation that lacks this scaling component. The discharge prediction is significantly improved when the closure relation is calibrated against the observed discharge, resulting in local-scale GRU-properties optimal for the predictions. Calibration was done by changing one local-scale observable, i.e. hydraulic conductivity (Ks), using a single pre-factor for the entire catchment. It is shown that the calibrated Ks values are somewhat comparable to the observed Ks values at a local scale in the study catchment. These results suggest that, in the absence of discharge observations, reasonable estimates of catchment-scale runoff responses can possibly be achieved with the observations at the sub-GRU (i.e. plot) scale. Our study provides a platform for the future development of low-dimensional, semi-distributed, physically based discharge models in ungauged catchments.

The geomorphological map is an important tool for studying land-surface processes. Automated mapping ensures a consistent mapping scheme with a reduced field survey time...