Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.
Prediction of river discharge is important for water resources management. Engineers have developed many physical and mathematical models for prediction of river discharge. The fact that physical hydrological models are site specific and include many parameters, has led researchers to work on mathematical black-box models. In this study, the fuzzy time series (FTS) method was used in the prediction of river discharge. The proposed method, which is employed for the first time in hydrology, allows to fast decision-making mechanism. The proposed algorithm, FTS, is used along with continuous wavelet transform (CWT) method to improve prediction performance. CWT, can be used as pre-treatment technique, is able decompose concerned time series into several bands at different scales which allows to predict much more homogeneous series rather than complex flow discharge series. By considering various statistical success criteria, the wavelet transformed time series (WFTS) method performed quite high accurate predictions compared to the classical fuzzy time series method. Combining FTS with wavelet transform opens a new window in the fuzzy time series method applications that has ability to improve the prediction performance.
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