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    Wyświetlanie 1-2 z 2
    Tytuł:
    Implementation of Distributed Hydrological Modeling in a Semi-Arid Mediterranean Catchment Azzaba, Morocco
    Autorzy:
    Abdelmounim, Bouadila
    Benaabidate, Lahcen
    Bouizrou, Ismail
    Aqnouy, Mourad
    Powiązania:
    https://bibliotekanauki.pl/articles/123743.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    semi-arid mediterranean context
    flood forecasting
    continuous modelling
    event-based modelling
    ATHYS platform
    distributed SCS-LR model
    Opis:
    The typical Mediterranean climate is marked at certain times of the year by sudden torrential rains causing high water flows, which leads to heavy flooding and hydroclimatic fluctuations due to a semi-arid climate. This explains the need for hydrological modeling for water resource management in these contexts. This work concerns the hydrological modeling of the Azzaba catchment area in Haut-Sebou “Morocco”. In the first part of this work, a bibliographic synthesis was carried out to characterize certain factors (physical, geological and climatic), and a hydrological study was carried out by processing rainfall and hydrometric data from the considered time periods. Ultimately, the use of the “ATHYS” platform is beginning to reproduce the flows at the Azzaba outlet. This model is really applicable in the semi-arid context based on several studies carried out on these contexts, since it has to consider the chronological sequence of phenomena on one hand and the influence of the climatic and physicalhydrogeological parameters of the basin (humidity and soil exchange) on the other. Several criteria were used in this study to estimate the model performance; the most common is Nash-Sutcliffe. After observation and analysis of the overall results, it can be concluded that the model reproduces flows in the Azzaba River watershed well, especially in event mode (mean Nash-Sutcliffe value of 0.71). The use of a historical meteorological time series to simulate flow using a daily time step gives average results with a Nash of 0.50, which strengthens the reliability of the ATHYS platform in the Mediterranean climate area.
    Źródło:
    Journal of Ecological Engineering; 2019, 20, 6; 236-254
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data-driven discharge analysis: a case study for the Wernersbach catchment, Germany
    Autorzy:
    Popat, Eklavyya
    Kuleshov, Alexey
    Kronenberg, Rico
    Bernhofer, Christian
    Powiązania:
    https://bibliotekanauki.pl/articles/108441.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
    Tematy:
    artificial neural networks
    data-driven modelling
    event-based coefficient of rainfall-runoff
    precipitation
    multi-correlation analysis
    soil moisture content
    Opis:
    This study focuses on precipitationdischarge data-driven models, with regression analysis between the weighted maximum rainfall and maximum discharge of flood events. It is also the first of its kind investigation for the Wernersbach catchment, which incorporates data-driven models in order to evaluate the suitability of the model in simulating the discharge from the catchment and provide good insights for future studies. The input parameters are hydrological and climate data collected from 2001 to 2009, including precipitation, rainfall-runoff and soil moisture. The statistical regression and artificial neural network models used are based on a data-driven multiple linear regression technique, and the same input parameters are applied for validation and calibration. The artificial neural network model has one hidden layer with a sigmoidal activation function and uses a linear activation function in the output layer. The artificial neural network is observed to model 0.7% and 0.5% of values, with and without extreme values respectively. With less than 1% error, the artificial neural network is observed to predict extreme events better compared to the conventional statistical regression model and is also better suited to the tasks of rainfall-runoff and flood forecasting. It is presumed that in the future this study’s conclusions would form the basis for more complex and detailed studies for the same catchment area.
    Źródło:
    Meteorology Hydrology and Water Management. Research and Operational Applications; 2020, 8, 1; 54-62
    2299-3835
    2353-5652
    Pojawia się w:
    Meteorology Hydrology and Water Management. Research and Operational Applications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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