Ahmad Abu Arra

Drought evaluation is a key component of water resources management, commonly performed using standardized drought indices (SDIs) based on probabilistic standardization of different input variables. While widely accepted for their simplicity, SDIs involve uncertainties, particularly related to the selection of the observation period and the parameters of the probability distribution function. One often overlooked methodological aspect is the fitting process: it can be applied to the entire dataset (classical model – C) or separately for each month (dynamic model – D), resulting in 1 or 12 models, respectively. This distinction has not been thoroughly addressed in the literature. To fill this gap, this study investigates the differences between classical and dynamic SPI models across various timescales using statistical metrics, drought classification, drought characteristics, and drought events. The analysis was applied to the Antalya Basin in Türkiye using monthly precipitation data from 1969 to 2022. Results reveal that the dynamic model produces significantly longer drought durations at short and medium timescales. For instance, drought duration for C-SPI 6 ranged between 4 and 6 months, while D-SPI 6 ranged between 6 and 8 months – indicating up to a 50% increase. These findings highlight the importance of model choice for accurate drought assessment.

The existing literature has studied and addressed the limitations of traditional drought evaluation methods, which often depend on one station without considering the spatiotemporal integration, resulting in an incomplete drought assessment. Given these limitations, this research proposes a new approach using the Specific Period (SP) and Precipitation Index (PI) concepts and aims to provide new perspectives for drought analysis. The methodology focuses on integrating all stations within the study area, allowing for a more comprehensive understanding of the evolution and characteristics of drought at each month. The Standardized Precipitation Index (SPI) at Konya Endorheic Basin (KEB) is used in this research to define drought events at multiple time scales, both for the SPI and Run theories. The main objective is to develop an innovative holistic framework for drought evaluation. The results demonstrate that the new approach improves the accuracy and consistency of drought detection compared to traditional methods. The results showed that drought durations ranged from 23 to 29 months for SPI-12 in regions such as Cihanbeyli, Ereğli, and Seydisehir. In contrast, regions such as Aksaray and Konya Havalimanı emerged as the least affected, with positive PI values between +0.14 and +0.19, compared to negative values of −0.71 to −0.83 in Seydisehir, Ereğli, and Cihanbeyli, indicating spatial variations in drought evolution within the KEB. This research provides a more comprehensive framework for monitoring drought in semi-arid regions, supporting water resource management policies and climate change adaptation and mitigation plans.

The increasing droughts and climate change effects and their frequencies worldwide are a critical threat, especially to regions facing water scarcity and wars. Therefore, comprehensive drought evaluation and trend analysis are crucial for water resources management, climate change, and drought mitigation plans. Classical drought evaluation methods predominantly rely on in situ observations, often limited or unavailable in many regions, particularly in developing countries such as Palestine. This study investigates the temporal and spatial characteristics and trends of drought across Palestine between 1940 and 2025. To the best of our knowledge, for the first time in the literature, bias-corrected ERA5 precipitation data are employed alongside ground-based observations to assess drought using the Standardized Precipitation Index (SPI) at multiple timescales (1-, 6-, and 12-month). Trend detection was performed through conventional statistical approaches, including the Mann–Kendall test, Spearman’s Rho, and Sen’s slope (SS), as well as the Frequency-Innovative Trend Analysis (F-ITA) method. Furthermore, the performance of the original and bias-corrected ERA5 precipitation datasets was evaluated against observational data using statistical metrics. The main findings indicated that the bias correction significantly improves the accuracy of the ERA5 precipitation data. Also, droughts in SPI-1 and SPI-6 ranged from 4 to 5 months, the minimum at which a drought can be classified. In addition, the average drought duration at a 12-month timescale ranged between 14 and 16 months. At short (SPI-1) and medium (SPI-6) timescales, no significant trends were found, whereas at the long timescale (SPI-12) all stations showed a significant decreasing SPI trend, such as −5.611 in Jenin, reflecting intensifying drought conditions. For F-ITA, the frequencies of extreme drought classification increased from 0.4% in the first period to 2.18% in the second period. The findings of this research have important implications for drought management, water policy planning, and climate adaptation in Palestine.

Given the growing adverse effects of drought on water resources, agriculture, and various sectors, assessing and evaluating drought and producing high-quality drought maps despite the data scarcity to better understand its impacts and develop effective mitigation strategies is essential. Considering the existing gaps related to drought evaluation, especially in scarce data regions, this research aims to evaluate the efficiency of acceptable time period for drought studies (10–20 years), evaluate the performance of ERA5-LAND and IMERG-NASA precipitation data in estimating the Standardized Precipitation Index (SPI) using different statistical metrics and the innovative drought classification matrix (IDCM), and finally produce and compare high-quality and accurate drought characteristics maps resulted from in situ stations, ERA5-LAND, and IMERG-NASA. The Kocaeli province in Türkiye, which has limited data and is a scarce data region, has been selected as an application. The results ensure that an acceptable time period can be sufficient and provide reliable accuracy for assessing drought with RMSE ranging between 0.09 and 0.23 standard deviation and IDCM ranging between 85% and 97%. NASA IMERG data gave more accurate drought results than ERA5-LAND, and the Pearson correlation ranges between 0.57 and 0.89. Also, in situ data showed longer drought duration, while ERA5-LAND and NASA had higher intensity. This article enables policymakers and decision-makers to manage and plan water resources within the city boundary, ensuring sustainable agricultural, economic, and industrial activities and supporting effective climate change adaptation strategies.

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying theoretical drought CDF models, such oversights lead to biased representations of drought evaluation and characteristics. This research compares the parametric theoretical and empirical CDFs for a comprehensive evaluation of standardized Drought Indices. Additionally, it examines the advantages, disadvantages, and limitations of both empirical and theoretical distribution functions in drought assessment. Three drought indices, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Standardized Precipitation Evapotranspiration Index (SPEI), cover meteorological and hydrological droughts. The assessment spans diverse applications, covering different climates and regions: Durham, United Kingdom (SPEI, 1868–2021); Konya, Türkiye (SPI, 1964–2022); and Lüleburgaz, Türkiye (SDI, 1957–2015). The findings reveal that theoretical and empirical CDFs demonstrated notable discrepancies, particularly in long-term hydrological drought assessments, where underestimations reached up to 50%, posing risks of misinformed conclusions that may impact critical drought-related decisions and policymaking. Root Mean Squared Error (RMSE) for SPI3 between empirical and best-fitted CDF was 0.087, and between empirical and Gamma it was 0.152. For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. Additionally, it concludes that empirical CDFs provide more reliable and conservative drought assessments and are free from the constraints of model assumptions. Both approaches gave approximately the same drought duration with different intensities regarding drought characteristics. Due to the complex process of drought events and different definitions of drought events, each drought event must be studied separately, considering its effects on different sectors.

Soil erosion due to rainfall is one of the primary soil degradation challenges and issues worldwide and significantly affects water and soil resources management, agricultural protection, and many other sectors. Among several models used for soil erosion estimation, the Revised Universal Soil Loss Equation (RUSLE) model is among the most effective. In the RUSLE model, the R-factor represents the rainfall erosivity, significantly affecting soil erosion results. However, the RUSLE model requires high spatial-resolution precipitation datasets. Still, it has some limitations, such as challenges with continuous data, particularly in developing countries, data scarcity regions, and inadequate spatial coverage, indicating the importance of using satellite and reanalysis datasets. In numerous studies, these datasets were used without validation and performance evaluation. These issues prevent researchers and local and global governments from comprehensively analyzing and estimating soil erosion and its adverse effects. This research aims to evaluate the performance of ERA 5-LAND in soil erosion estimation using the RUSLE model over the Isser basin in Algeria as an application based on the total amount of soil loss, descriptive statistics, and statistical metrics. To the best of our knowledge, this research evaluates the performance of ERA 5-LAND for soil erosion calculation for the first time. The results showed that ERA 5-LAND precipitation data underestimated soil erosion by about 24% for the second scenario by selecting the nearest sixteen grid points and 38% for the third scenario by selecting all grid points. The annual average estimated soil erosion for in-situ rain gauge stations, ERA 5-LAND grid points corresponding to the rain gauge stations, and all ERA 5-LAND grid points, 62.18-, 47.03-, and 38.40-ton ha⁻¹ year⁻¹, respectively. Also, RMSE results showed slightly high errors, indicating that the ERA 5-LAND needs more correction and enhancement. Even so, ERA 5-LAND can generally be used with acceptable reliability and accuracy, especially in developing countries facing data availability problems.