Civil Engineering / İnşaat Mühendisliği

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

Browse

Search Results

Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 3
    Citation - Scopus: 2
    Assessing 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.
  • Annotation
    Citation - WoS: 1
    Citation - Scopus: 1
    Closure 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: 103
    Citation - Scopus: 126
    Ann 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.