Evaluating drought is paramount in water resources management and drought mitigation plans. Drought indices are essential tools in this evaluation, which mainly depends on the time period of the original datasets. Investigating the effects of time periods is critical for a comprehensive understanding and evaluation of drought. Also, It holds particular significance for regions facing data availability challenges. The existing literature reveals a gap in drought assessment and comparison analysis using conventional methods based on drought indices only. This research proposes an innovative drought classification matrix to compare drought indices and spatial and temporal scenarios; the proposed matrix depends on any drought classification for comparison procedure. Furthermore, it aims to investigate the differences between several time period scenarios based on the proposed matrix and several statistical metrics (R², CC, RMSE, HH, and RB) and determine the acceptable/minimum time period. The application of the proposed matrix and selection of an acceptable/minimum time period is presented to three different climates: Durham station in the United Kingdom, Florya station in Türkiye, and Karapinar station in Türkiye. The results show that the time period scenarios are able to catch the reference time period (RTP) scenario reasonably, with strong correlation and negative relative bias. The 10-year time period is sufficient as an acceptable/minimum time period for short timescales, such as meteorological drought. Conversely, for longer timescales, such as hydrological drought, a 20-year time period is the acceptable/minimum time period. The proposed matrix demonstrates a robust and powerful framework for comparison, making it applicable to various drought assessment scenarios globally.

As a catastrophic phenomenon, drought has destructive impacts on water resources, the environment, and the ecosystem. Consequently, drought plays a vital role in risk assessment, water resources management, and drought mitigation plans. The main aim of this research is to obtain critical intensity-duration-frequency (IDF) drought curves and to provide a comprehensive understanding of the drought characteristics by considering the meteorological Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological Standardized Streamflow Index (SSI). Critical IDF curves for the drought index and return period selection are identified. Also, new terms are defined as the specific drought duration, the maximum drought duration, and the critical intensity based on drought IDF curves. The results show that the SPI3 based on run theory for 500 years return period has higher drought intensity compared with other drought indices. In some IDF curves, the 2-year return period of a 12-month duration timescale is not provided. Regarding the maximum drought duration, the SPEI12 gave a longer duration. With the new concepts in this research, the presented IDF drought methodology has a novel additional practice to identify the critical intensity and maximum drought duration. Using this methodology for any drought index will contribute to converting data with mathematical calculations into IDF curves for design and risk assessment purposes.

As a natural disaster, drought has inverse effects on the agricultural and environmental sectors. Comprehensive drought evaluation is critical for integrated water resources management and drought monitoring. Drought is generally evaluated temporally and spatially without considering its trend, but in recent years, the study of drought trends has become common. However, in drought trend analyses, it is necessary to identify the frequency change of drought classification, along with the sub-trends and their magnitudes, which have been identified in this research with the combined frequency analysis and innovative trend analysis methodologies (F-ITA). Three different drought indices, the standardized precipitation index (SPI), Standardized precipitation evapotranspiration index (SPEI), and streamflow drought index (SDI), were calculated at different time scales at Florya Station, Istanbul, Türkiye, Durham Station in the United Kingdom, and Vargonas (Vargönäs) KRV Station in Sweden. The results showed that F-ITA improved the drought evaluation with a more detailed investigation of meteorological and hydrological drought trends on a microscale rather than stating a decrease or increase trend. There was no trend for SPI-3 and -6 F-ITA; for SPI-12 F-ITA, all drought classifications showed a monotonic increase trend. For example, for SPI-1 and SPEI-1 F-ITA, the abnormally dry classification showed minimal change with frequencies of 35%. The SPEI-12 F-ITA graph showed an increasing trend in all drought classifications. For SPEI-12, the frequency of exceptional drought (extremely dry) events increased from 0.11% (0.66%) to 1.21% (2.3%), while severe and moderate drought frequencies rose significantly from 3.73% to 4.61% and 7.46% to 11.4%, respectively. Finally, monotonic trend increases were noticed in the SDI at all time scales for all classifications. Graphical abstract

Research and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.