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Hydrology

Hydrology is an international, peer-reviewed, open access journal on hydrology published monthly online by MDPI.
The American Institute of Hydrology (AIH) and Japanese Society of Physical Hydrology (JSPH) are affiliated with Hydrology and their members receive discounts on the article processing charges.
Quartile Ranking JCR - Q2 (Water Resources)

All Articles (1,574)

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications.

17 November 2025

Overall diagram of the proposed ubiquitous, green, and fast urban flood water depth sensing method.

Flash Drought Assessment: Insights from a Selection of Mediterranean Islands, Greece

  • Chrysoula Katsora,
  • Evangelos Leivadiotis and
  • Nektaria Papadopoulou
  • + 5 authors

Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, Lemnos, Lesvos and Samos). Hourly soil moisture data, spanning from 1990 to the present, covering three soil layers (0–7 cm, 7–28 cm and 28–100 cm), were analyzed and mapped onto a 0.1° × 0.1° grid with a native resolution of approximately 9 km. Additionally, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied to the island of Lesvos, using precipitation and average temperature data from the local meteorological stations. The number and characteristics of these events—including frequency, duration, decline rate, magnitude, intensity, recovery rate and recovery duration—were produced to construct a regional overview of FD risk across the Northeastern Aegean Islands. These results reveal a considerable variability in the spatial, seasonal and temporal distribution of past FD events. Furthermore, this study highlights the value of using satellite-derived soil moisture data for identifying FD events and demonstrates that analyzing this data with field temperature and precipitation measurements enables a more localized and accurate interpretation of past events. This approach facilitates the definition of FD “hotspot” areas, which, when combined with further investigation, can lead to the development of a predictive FD model.

18 November 2025

Estimated groundwater recharge is considered the essential factor for groundwater management and sustainability, especially in arid lands such as the Kingdom of Saudi Arabia (KSA). Consequently, assessing groundwater recharge is a key process for forecasting groundwater accessibility to sustain safe withdrawal. So, this study focused on environmental isotopes, the chloride mass balance (CMB) method, and a SWAT model by integrating GIS with hydrological and hydrochemical techniques to detect the origin of coastal aquifer groundwater and to compute the recharging rate in the study area. This study is based on the results of chemical analysis of 78 groundwater samples and environmentally stable isotopes, including deuterium (2H) and oxygen-18O, in 29 representative samples. The results revealed that the origin of groundwater recharge comes through precipitation, where the ranges of δ18O and δ2H isotopes in the analyzed groundwater were from −1.10‰ to +1.03‰ and from −0.63‰ to 11.63‰, respectively. The CMB finding for estimating the average recharge is 3.57% of rainfall, which agrees with a previous study conducted in the wadi Qanunah basin (north of the study area), where the estimated average value of recharge was 4.25% of rainfall. Meanwhile, the estimated annual recharge using a SWAT model ranged between 1 mm and 16.5 mm/year at an average value of approximately 8.75 mm/year. The results obtained by the two techniques are different due to some reasons such as the presence of additional chloride sources, as well as evaporation. Outputs of this study will be valuable for the local community, officials, and decision-makers who are concerned with groundwater resources.

16 November 2025

Rain-on-snow (ROS) events profoundly influence mixed rain–snow flooding and the water resource cycle. However, current research regarding ROS events remains predominantly reliant on existing datasets, lacking detailed controlled experiments under variable conditions. This study employed control variables and an orthogonal experimental design to conduct laboratory-controlled experiments simulating ROS events with different temperatures, rainfall intensities, and rainfall durations. Observations and analyses were performed on the snowmelt volumes during and after events. The results indicate that ROS events significantly accelerate snowmelt rates and increase total snowmelt volume. Under low-intensity ROS, snowmelt volume exhibits greater sensitivity to temperature changes. A temperature threshold exists between 2 °C and 6 °C; beyond this threshold, the melting rate accelerates and ablation volume increases. Under high-intensity ROS, rainwater becomes the dominant factor driving snowpack ablation. When rainfall intensity exceeds 60 mm·h−1, it triggers a sharp increase in snowmelt volume. Concurrently, following an ROS event, snowpacks subjected to low-intensity rainfall exhibit a stronger rainwater retention capacity, an effect that becomes more pronounced at lower temperatures. Additionally, snowmelt volume increases with prolonged rainfall duration, with the increment in snowmelt volume attributable to extended rainfall time being greater under weaker rainfall intensities. These findings provide a scientific reference for better understanding ROS-related disasters mechanisms.

16 November 2025

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Hydrology - ISSN 2306-5338