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.

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANNs) are proven to be suitable for predicting reference evapotranspiration (ETo) and ETa based on RS data. This study aims to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables in a large irrigation catchment. The ANN model has been applied to the two different scenarios. Data from only the 38 days of satellite overpass dates was selected in Scenario I, while in Scenario II all datasets, i.e., the 769-day data were used. An irrigation scheme, located in the Mediterranean region of Turkiye, was selected, and a total of 38 Landsat images and local climatic data collected in 2021 and 2022 were used in the ANN model. The ETa results by the ANN model for Scenarios I and II showed that the R² values for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84) were all remarkably high. Moreover, the results of the new ANN model in two scenarios showed an acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential for obtaining daily ETa using limited climatic data and RS imagery. As a result, the suggested ANN model for daily ETa computation offers a trustworthy way to determine crop water usage in real time for sustainable water management in agriculture. It may also be used to assess how crop evapotranspiration in drought-prone areas will be affected by climate change in the 21st century.

Estimating actual evapotranspiration (ETa) rates for different crop types grown in large irrigation schemes is of great importance to improve water use efficiency. To that end, crop types over large irrigation areas can be determined by employing remote sensing techniques coupled with ground truth data. This study aimed to: a) conduct a water balance work by realizing in-situ water balance measurements, b) derive cropping pattern maps, c) acquire daily crop evapotranspiration (ETc), d) generate fully distributed ETa maps by remote sensing, and e) compare ETa estimations with ETc and in-situ water balance. Landsat-7 and -8 satellite data and meteorological data were used to estimate ETa in the Eastern Mediterranean Region of Turkey in the 2021 hydrological year. Crop types were classified using Sentinel 2A-2B satellite images. The change in catchment storage was +67.08 mm with a 3.34% water balance closure error. The correlation between ETa and ETc for irrigated wheat, potato, lettuce, corn-1, peanut-1, soybean-2, and citrus was significant in growing season.