Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

The movement of stones is important in a variety of disciplines such as geomorphology or hydraulic engineering. Plenty of different sensors, visual, active or passive tracers exist to capture movements in various ways. However, none of them is sufficiently small to be implanted in pebbles with a longest axis of approx. 60mm.In this article, a sufficiently small probe is introduced: the Smartstone probe. It consists of a metal cylinder (diameter 8mm, length 55mm) with a flexible antenna and contains a Bosch BMX055 sensor composed of a triaxial accelerometer, magnetometer and gyroscope, respectively. Additional components inside the probe are memory to store data, active RFID (Radio-frequency identification) technique to transmit data and two button cells as power supply.Mounted into a pebble, the applicability of this probe was tested in laboratory flume experiments by determining the pebble movement using the Smartstone measurements and comparing them to the movement pattern captured by a high-speed camera. The derived orientations and positions in these test experiments resulted in deviations of 32.4% compared to the visual footage. The different reasons for deviations are noise, quantization error, integration error, orientation error and clipping. The error sources were divided with supplementary experiments resulting in mean absolute deviation (MAE) of 3.3% due to noise, quantization, and integration errors; orientation errors result in an increased MAE of 13.7% in natural environment and 21.7% in laboratory. The MAE of all experiments containing clipping was 63.2%.These deviations will be reduced in future by application of methods like Kalman filtering or Markov models, which are established in other disciplines like computer science, robotics or (pedestrian) navigation

To precisely map the changes in hydrologic response of catchments (e.g. water balance, reactivity or extremes), we need sensitive and interpretable indicators. In this study we defined nine hydrologically meaningful signature indices: five indices were sampled on the flow duration curve, four indices were closely linked to the distribution of event runoff coefficients. We applied these signature indices to the output from a hydrologic catchment model for three different catchments located in the Nahe basin (Western Germany) to detect differences in runoff behavior resulting from different meteorological input data. The models were driven by measured and simulated (COSMO-CLM) meteorological data. It could be shown that the application of signature indices is a very sensitive tool to assess differences in simulated runoff behavior resulting from climatic data sets of different sources. Specifically, the selected signature indices allow assessing changes in water balance, vertical water distribution, reactivity, seasonality and runoff generation. These indices showed that the hydrological model is very sensitive to biases in mean and spatio-temporal distribution of precipitation and temperature because it acts as a filter for the meteorological input. Besides model calibration and model structural deficits, we found that bias correction of temperature fields and further adjustment of bias correction of precipitation fields is absolutely essential. We conclude that signature indices can act as indirect "efficiency measures" or "similarity measures" for output from regional or local climate models.