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Accurate real-time forecasting of river water level is an important issue that has to be addressed in order to prevent and mitigate water-related risk. To this end, data assimilation methods have been used to improve the forecasts ability of water model merging observations coming from stations and model simulations. As a consequence of the increasing availability of dynamic and cheap sensors, having variable life-span, space and temporal coverage, the citizens are becoming an active part in information capturing, evaluation and communication. On the other hand, it is difficult to assess the uncertain related to the observation coming from such sensors. The main objective of this work is to evaluate the influence of the observational error in the proposed assimilation methodologies used to update the hydrological model as response of dynamic observations of water discharge. We tested the developed approaches on a test study area - the Brue catchment, located in the South West of England, UK. Two different filtering approaches, Ensemble Kalman filter and Particle filter, were applied to the semi-distributed hydrological model. Discharge observations were synthetically generated as a function of the observed and simulated value of flow at the basin outlet. Different types of observational error were introduced assuming diverse sets of probability distributions, first and second order moments. The results of this work show how the assimilation of dynamic observations, in time and space, can improve the hydrologic model performance with a better forecast of flood events. It was found that the choice of the appropriate observational error, of difficult characterization, and type of filtering approach affects the model accuracy. This study is partly supported by the FP7 EU Project WeSenseIt.


Session R48, Data Processing: Data to Computations



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