Abstract. Hydrological simulation in regions with a large number of water storages is difficult due to inaccurate water storage data. To address this issue, this paper presents an improved version of SWAT2005 (Soil and Water Assessment Tool, version 2005) using Landsat, a satellite-based dataset, an empirical storage classification method and some empirical relationships to estimate water storage and release from the various sizes of flow detention and regulation facilities. The SWAT2005 is enhanced by three features: (1) a realistic representation of the relationships between the surface area and volume of each type of water storages, ranging from small-sized flow detention ponds to medium-and large-sized reservoirs with the various flow regulation functions; (2) water balance and transport through a network combining both sequential and parallel streams and storage links; and (3) calibrations for both physical and human interference parameters. Through a real-world watershed case study, it is found that the improved SWAT2005 more accurately models small-and medium-sized storages than the original model in reproducing streamflows in the watershed. The improved SWAT2005 can be an effective tool to assess the impact of water storage on hydrologic processes, which has not been well addressed in the current modelling exercises.
Abstract. Accurate lead-time forecast of ice breakup is one of the key aspects for ice flood prevention and reducing losses. In this paper, a new data-driven model based on the Statistical Learning Theory was employed for ice breakup prediction. The model, known as Support Vector Machine (SVM), follows the principle that aims at minimizing the structural risk rather than the empirical risk. In order to estimate the appropriate parameters of the SVM, Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is performed through exponential transformation. A case study was conducted in the reach of the Yellow River. Results from the proposed model showed a promising performance compared with that from artificial neural network, so the model can be considered as an alternative and practical tool for ice breakup forecast.
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