Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
Browse
5 results
Search Results
Article Citation - WoS: 3Citation - Scopus: 2Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan(Birkhauser, 2025) Tayfur, G.; Hayat, E.; Safari, M.J.S.Afghanistan is suffering from periodic events of drought, which has exacerbated in recent years due to extreme climate events in the region. Having an arid to semi-arid climate, the country faces significant challenges of water resources management, especially for irrigation as reliance on agriculture is cumbersome. This study is undertaken to characterize historical meteorological drought in Afghanistan to provide an insight on where and when meteorological drought events happened in different River Basins (RBs). The study mainly employs the gamma-Standardized Precipitation Index (gamma-SPI) to analyze historical meteorological droughts across Afghanistan from 1979 to 2019. Monthly precipitation data is obtained from the Ministry of Energy and Water (MEW) of Afghanistan, which is a combination of observed data from ground stations and gap-filled data by the MEW for the study period. Gridded gamma-SPI values are interpolated and mapped to visualize patterns of spatial drought across the entire country. The results indicate that countrywide extreme drought events occurred in 1999, 2000, 2001, 2010, 2016, 2017, and 2019, particularly affecting southern, western, and southwestern regions. Decreasing rainfall occurred in all five RBs, with the most considerable decline observed in the 1999–2008 period. The study reveals the increasing frequency and severity of meteorological droughts in Afghanistan. It also emphasizes on the vulnerability of agriculture and water sectors due to the drought events. The findings of the study suggest the need for better drought monitoring, preparedness, awareness, and adaptation of strategies to ensure water security and agricultural sustainability in the face of climate change. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Article Citation - WoS: 35Citation - Scopus: 36Coupling Soil Moisture and Precipitation Observations for Predicting Hourly Runoff at Small Catchment Scale(Elsevier Ltd., 2014) Tayfur, Gökmen; Zucco, Graziano; Brocca, Luca; Moramarco, TommasoThe 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: 22Citation - Scopus: 26Fuzzy 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.Annotation Citation - WoS: 1Citation - Scopus: 1Closure To "ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff" by Gokmen Tayfur and Vijay P. Singh(American Society of Civil Engineers (ASCE), 2008) Tayfur, Gökmen; Singh, Vijay P.We would like to thank Dr. Wong for his interest in and thoughts on our analysis of runoff hydrograph prediction and the goodnessof-fit measurement. We agree that visual comparison of simulated and measured hydrographs is an important indicator for assessing the performance of models. Visual inspection allows one to see intricate differences between hydrographs.Article Citation - WoS: 103Citation - Scopus: 126Ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff(American Society of Civil Engineers (ASCE), 2006) Tayfur, Gökmen; Singh, Vijay P.This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.