A methodology using the standardised precipitation index is proposed to develop critical drought intensity-duration-frequency (IDF) curves. We define dry periods within which we recognise droughts of different durations. The most severe drought for each drought duration in each year is called the critical drought. The total probability theorem-coupled frequency analysis is used to determine the best-fit probability distribution function of drought severity, which is then converted to intensity. The generalised extreme value probability distribution function is found to best fit the critical drought severity. The methodology is implemented using monthly precipitation data for a meteorological station in Turkey. The critical drought intensity decreases linearly with increasing drought duration, whereas the return period increases exponentially when the drought becomes more severe. The site-specific IDF curves furnished with an empirical relationship between the intensity and return period allow one to characterise the drought not by an index-based intensity but by its return period. This kind of presentation is physically easier to understand, in particular for stakeholders and decision makers in practice.

This study attempts to find out the best-fit probability distribution function to low flows using the up-to-date data of intermittent and non-intermittent rivers in four hydrological basins from different regions in Turkey. Frequency analysis of D = 1, 7, 14, 30, 90 and 273-day low flows calculated from the daily flow time series of each stream gauge was performed. Weibull (W2), Gamma (G2), Generalized Extreme Value (GEV) and Log-Normal (LN2) are selected among the 2-parameter probability distribution functions together with the Weibull (W3), Gamma (G3) and Log-Normal (LN3) from the 3-parameter probability distribution functions family. Selected probability distribution functions are checked for their suitability to fit each D-day low flow sequence. LN3 mostly conforms low flows by being the best-fit among the selected probability distribution functions in three out of four hydrological basins while W3 fits low flows in one basin. With the use of the best-fit probability distribution function, the low flow-duration-frequency curves are determined which has the ability to provide the end-users with any D-day low flow discharge of any given return period.

Geographic Information Systems (GIS) are widely used in most studies on water resources. Especially, when the topography and geomorphology of study area are considered, GIS can ease the work load. Detailed data should be used in this kind of studies. Because of, either the complication of the models or the requirement of highly detailed data, model outputs can be obtained fast only with a good optimization. The aim in this study, firstly, is to determine flood-prone areas in a watershed by using a hydrological model considering two wetness indexes; the topographical wetness index, and the SAGA (System for Automated Geoscientific Analyses) wetness index. The wetness indexes were obtained in the Quantum GIS (QGIS) software by using the Digital Elevation Model of the study area. Flood-prone areas are determined by considering the wetness index maps of the watershed. As the second stage of this study, a hydraulic model, HEC-RAS, was executed to determine flood inundation areas under different return period-flood events. River network cross-sections required for this study were derived from highly detailed digital elevation models by QGIS. Also river hydraulic parameters were used in the hydraulic model. Modelling technology used in this study is made of freely available open source softwares. Based on case studies performed on watersheds in Turkey, it is concluded that results of such studies can be used for taking precaution measures against life and monetary losses due to floods in urban areas particularly.

Hydropower is a sustainable and renewable energy source that can serve as a practical and economically viable solution to the future possible energy crisis and climate change scenarios. Moreover, it possesses a higher energy density compared to other alternative renewable energy sources such as solar, wind energy, etc. In order to determine the potential of hydropower, long-term observed hydrometeorological data of streamflow, precipitation, etc. are crucial. This study investigates a new power duration curve (PDC) methodology. Basin characteristics such as drainage area and basin relief with meteorological data as precipitation are used for regional models in the application. The classification based on geographical locations is made for regional models. Six models based on equation type were utilized to determine the optimal regional model. Absolute errors of cease-to-flow point estimates ranging from 0.01 to 11.49% were observed. The model provided successful results according to Nash–Sutcliffe efficiency which is widely used in hydrological studies very close to 1 and higher than 0.87 except for one streamflow gauging station therewithal all other calculated performance metrics. It is observed that power and cease-to-flow point estimates of intermittent rivers can be obtained with a new PDC model based on basin characteristics.

The construction sector in Iran faces substantial inefficiencies, including high material wastage, posing environmental and economic risks. This study investigated the adoption of Lean Construction (LC) practices and AI/IoT technologies in Iran’s public construction sector using a mixed-methods approach. This research examined the organizational, technical, and infrastructural factors across four key provinces—Tehran, Isfahan, Khorasan Razavi, and Fars—and employed fuzzy logic to address the uncertainties in adoption decisions. Data from 28 key stakeholder interviews were analyzed using Python 3.9, with libraries such as Pandas 1.3.3, NumPy 1.21.2, and skfuzzy 0.4.2 for the statistical analysis and NVivo 12 for the thematic coding. The analysis revealed that organizational readiness and leadership support were the critical drivers of adoption, particularly in Isfahan and Khorasan Razavi, which exhibited the highest adoption likelihood scores (0.5000). Tehran and Fars showed slightly lower scores due to regulatory barriers and financial limitations. The findings highlight the need for targeted leadership training, regulatory reforms, and infrastructure investments to accelerate the adoption of these technologies. This study aligned with the Sustainable Development Goals (SDG 9: Industry, Innovation, and Infrastructure and SDG 11: Sustainable Cities and Communities) by offering practical recommendations for advancing sustainable practices in Iran’s construction sector. The insights provided have broader implications for other developing economies facing similar challenges, contributing to global efforts toward sustainable development.