Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Subject: search.filters.subject.RainfallWoS Q: search.filters.wosQuartile.Q1

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  • Article
    Citation - WoS: 35
    Citation - Scopus: 36
    Coupling Soil Moisture and Precipitation Observations for Predicting Hourly Runoff at Small Catchment Scale
    (Elsevier Ltd., 2014) Tayfur, Gökmen; Zucco, Graziano; Brocca, Luca; Moramarco, Tommaso
    The importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40cm soil depths along with rainfall in predicting runoff. For this purpose, two small sub-catchments of Tiber River Basin, in Italy, were instrumented during periods of October 2002-March 2003 and January-April 2004. Colorso Basin is about 13km2 and Niccone basin 137km2. Rainfall plus soil moisture at 10, 20, and 40cm formed the input vector while the discharge was the target output in the model of generalized regression neural network (GRNN). The model for each basin was calibrated and tested using October 2002-March 2003 data. The calibrated and tested GRNN was then employed to predict runoff for each basin for the period of January-April 2004. The model performance was found to be satisfactory with determination coefficient, R2, equal to 0.87 and Nash-Sutcliffe efficiency, NS, equal to 0.86 in the validation phase for both catchments. The investigation of effects of soil moisture on runoff prediction revealed that the addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. The sensitivity analysis indicated that the use of soil moisture data at different depths allows to preserve the memory of the system thus having a similar effect of employing the past values of rainfall, but with improved GRNN performance.
  • Article
    Citation - WoS: 22
    Citation - Scopus: 26
    Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture
    (Kluwer Academic Publishers, 2015) Tayfur, G.; Brocca, L.
    This study developed Mamdani-type fuzzy logic model to simulate daily discharge as a function of soil moisture measured at three different depths (10, 20 and 40 cm) and rainfall. The model was applied to 13 km2 size Colorso Basin in central Italy for a period from October 2002 to April 2004. For each variable of soil moisture, rainfall, and discharge, 9 fuzzy subsets were employed while 30 fuzzy rules, relating the input variables (soil moisture and rainfall) to the output variable (discharge), were optimized. The model employed the min inferencing, max composition, and the centroid method. The model application results revealed that Mamdani-type fuzzy logic model can be employed to incorporate soil moisture along with rainfall to simulate discharge. Using soil moisture measured at 40 cm soil depth along with rainfall produced better simulation of discharge with NS=0.68 and R= 0.82. The performance of the model was also tested against a conceptual rainfall- runoff model of MISDc (Modello Idrologico Semi-Distribuito in continuo). MISDc couples an event-specific component with a module for continuous time soil water balance for taking into account the variable antecedent wetness conditions. The MISDc model requires estimation of seven parameters and the measurements of the hydrometeorological variables such as rainfall and air temperature. The comparative study revealed that fuzzy model performs better in capturing runoff peak rates and overall trend of high and small flooding events. © Springer Science+Business Media Dordrecht 2015.