Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin.

Land use and land cover change (LULCC) is considered one of the major drivers of climate change, although climate change can also foster direct or indirect influences leading to LULCC. The objective of the presented study is to offer a strategic observation frame as the land use and land cover (LULC) transitions are grouped to define the cover flows (CFs). The Küçük Menderes River Basin (KMRB), which is located in the west of Turkey was examined as the case study. Through LULCC modelling via the employment of multi-layer perceptron (MLP), cellular automata (CA), and Markov Chain methods, future LULC maps were projected up to the horizon of 2050. Hydrologic responses of the basin to LULCC were determined by the developed hydrologic model, which is generated by the Soil and Water Assessment Tool (SWAT). The superimposed impacts of the examined effects of LULCC have been investigated by the CF types. This way, the individual impacts of the CFs have been assessed. In the case of the KMRB, projected annual runoffs for the year 2050 cover map represent a 9.06% reduction and the major responsible CF type for this reduction is the conversion from forest to non-irrigated agricultural land cover by 22.90%.

Wetlands, as critical ecological systems, face increasing threats from anthropogenic pressures and climate change. This study investigates dynamic water allocation strategies for the restoration of Lake Marmara, a nationally important wetland within the Gediz River Basin of Türkiye, which has experienced complete desiccation in recent years. Within the scope of the PRIMA-funded “Mara-Mediterra” project, an integrated modeling approach was employed to evaluate multiple restoration scenarios using the WEAP (Water Evaluation and Planning) platform. Scenarios varied based on the initial storage capacity of Gördes Dam, irrigation demands, environmental flow priorities, and a potential water diversion investment from the Tabaklı reach. Results indicate that under current conditions, Lake Marmara’s ecological water needs can be sustained without the Tabaklı investment. However, under 2050 climate projections, scenarios lacking the Tabaklı investment or deprioritizing ecological needs consistently failed to meet the lake’s minimum water thresholds. Conversely, scenarios combining moderate dam storage levels, environmental prioritization, and Tabaklı inflow succeeded in restoring lake volumes by over 90%. These findings highlight the need for adaptive water planning that aligns with projected hydro-climatic shifts to ensure long-term wetland sustainability.

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modelling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the "Kaplan-Meier Estimator," a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to repurpose this methodology to assess drought risk by treating past droughts as "events" and evaluating the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) and map these results for a better understanding of the drought risks within larger spatial scales such as a river basin. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope get steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps which expose more vulnerable areas within the river basin.