Hydrology

29 pages, 3879 KiB

Open AccessArticle

Challenges and Limitations of Using Monitoring Data in Catchment-Based Models—A Case Study of Rivers Taw and Torridge, UK

by Richard Heal, Wayne Rostant and Paulette Posen

Hydrology 2025, 12(8), 212; https://doi.org/10.3390/hydrology12080212 - 12 Aug 2025

Abstract

Water quality monitoring is a key requirement for fulfilling various national environmental policies, but with many competing needs and limited resources, data collected can suffer from both spatial and temporal deficiencies. Modelling offers the potential to substitute estimated values into observational gaps, but [...] Read more.

Water quality monitoring is a key requirement for fulfilling various national environmental policies, but with many competing needs and limited resources, data collected can suffer from both spatial and temporal deficiencies. Modelling offers the potential to substitute estimated values into observational gaps, but model validation often requires the very data that are lacking. In this paper we present the results of a pilot study to investigate spatial and temporal issues around the monitoring of faecal indicator bacteria (Escherichia coli) in rivers of the Taw and Torridge catchments in the UK. Statistical analysis of in situ measurements versus simulated data from the catchment models reveals similar seasonal associations between riverine bacterial counts and rainfall patterns. Furthermore, spatial apportionment of livestock to better reflect land use was found to be important in the models, especially in upstream reaches of the catchments. In conclusion, successful monitoring of faecal bacteria levels in UK rivers requires risk-based monitoring (sufficient to identify possible seasonal trends) and informed spatial consideration of sampling sites. Catchment models can be useful aids for directing and augmenting such monitoring programmes, but these models should undergo rigorous validation, particularly in upper catchment areas, to ensure correct model response to changes in land use and/or climate. Full article

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17 pages, 3813 KiB

Open AccessArticle

Method for Establishing Heavy Rainfall Equations Based on Regional Characteristics: Transformation of Maximum Daily Precipitation

by Laura Thebit de Almeida, Roberto Avelino Cecílio, Marcel Carvalho Abreu and Ivana Patente Torres

Hydrology 2025, 12(8), 211; https://doi.org/10.3390/hydrology12080211 - 12 Aug 2025

Abstract

Modeling heavy rainfall patterns is essential for designing hydraulic structures, planning land use and water resources, and predicting disasters, among others. Usually, heavy rainfall is characterized by curves that relate the intensity, duration, and frequency (IDF), adjusted from the analysis of pluviograms. Alternatively, [...] Read more.

Modeling heavy rainfall patterns is essential for designing hydraulic structures, planning land use and water resources, and predicting disasters, among others. Usually, heavy rainfall is characterized by curves that relate the intensity, duration, and frequency (IDF), adjusted from the analysis of pluviograms. Alternatively, these equations can be adjusted using disaggregated daily rainfall data, whose reliability is currently questioned due to the use of common coefficients to disaggregate the maximum daily precipitation (h_day) into rainfall associated with shorter durations. This study proposes the Transformation of Maximum Daily Precipitation method (TMDP) using the maximum daily precipitation of the station of interest and the curve of heavy rainfall of the nearest location, denoting the local characteristic, to transform the h_day associated with a return period into rainfall intensities for shorter durations. The TMDP proved to be slightly superior to the most widely used rainfall disaggregation method in Brazil, particularly in regions with a higher density of data for the IDF equation. The TMDP is a potential tool for regions with low density of rainfall data, although it has limitations in regions where such data are scarce. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)

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15 pages, 2033 KiB

Open AccessArticle

Human-Induced Shifts in Yellow River Flooding: Population Threshold Effects in the Loess Plateau’s Primary Sediment Source Area (934 CE)

by Tao Huang and Yabin Li

Hydrology 2025, 12(8), 210; https://doi.org/10.3390/hydrology12080210 - 11 Aug 2025

Abstract

Flooding frequency in the lower Yellow River (YR) exhibited an abrupt increase post-934 CE, causing catastrophic societal disruptions. However, the quantitative relationship between this abrupt increase and the intensification of human activity in the midstream Loess Plateau (LP)’s Primary Sediment Source Area (PSSA) [...] Read more.

Flooding frequency in the lower Yellow River (YR) exhibited an abrupt increase post-934 CE, causing catastrophic societal disruptions. However, the quantitative relationship between this abrupt increase and the intensification of human activity in the midstream Loess Plateau (LP)’s Primary Sediment Source Area (PSSA) remains uncertain. This study systematically evaluates the threshold effects of human activities on YR flooding through multi-proxy historical records, GIS-based spatial analysis, and nonparametric statistical tests. The results show that from 934 to 1102 CE, the population density in the PSSA surged from 1.3 to 19.8 persons/km² (a 14.2-fold increase, p = 0.005). A 2400-year-scale comparison using 934 CE as the breakpoint revealed that the mean population density in this region increased from 5.2 to 51 persons/km² (a 9.8-fold increase). This dramatic population surge drove a 1.4-fold increase in the cultivation rate (from 8.6% to 20.5%), leading to a 5.4-fold rise in sediment yield (1.6 × 10⁸ → 1.02 × 10⁹ t/yr, p = 0.035), a 10-fold acceleration in downstream sedimentation rate (0.3 → 3.3 cm/yr, p = 0.001), and ultimately a 5.5-fold escalation in flooding frequency (from 1.6 to 10.4 events per 20 years, p < 0.0001). The study identifies 19.8 persons/km² as the ecological pressure threshold. It proposes converting population density to ecological pressure equivalents adjusted for soil–water conservation coverage (e.g., terracing/afforestation). When the equivalent ecological pressure exceeds 19.8 persons/km², pre-defined sediment control measures (e.g., tillage restrictions/afforestation mandates) should be enforced in the PSSA. Full article

(This article belongs to the Section Water Resources and Risk Management)

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16 pages, 2230 KiB

Open AccessArticle

Seismic Performance Assessment of Gravity Dams for Urban Flood Risk Mitigation Using the Scaled Boundary Finite Element Method (SBFEM)

by Min-koan Kim and Dai Xu

Hydrology 2025, 12(8), 209; https://doi.org/10.3390/hydrology12080209 - 10 Aug 2025

Abstract

Rapid urbanization and climate change have intensified urban flood risks, necessitating resilient upstream infrastructure to ensure metropolitan water security and effective flood mitigation. Gravity dams, as critical components of urban flood protection systems, regulate discharge to downstream urban areas. Gravity dams are critical [...] Read more.

Rapid urbanization and climate change have intensified urban flood risks, necessitating resilient upstream infrastructure to ensure metropolitan water security and effective flood mitigation. Gravity dams, as critical components of urban flood protection systems, regulate discharge to downstream urban areas. Gravity dams are critical for regulating flood discharge, yet their seismic vulnerability poses significant challenges, particularly under compound effects involving concurrent seismic loading and climate-induced elevated reservoir levels. This study introduces a novel seismic analysis framework for gravity dams using the scaled boundary finite element method (SBFEM), which efficiently models dam–water and dam–foundation interactions in infinite domains. A two-dimensional numerical model of a concrete gravity dam, subjected to realistic seismic loading, was developed and validated against analytical solutions and conventional finite element method (FEM) results, achieving discrepancies as low as 0.95% for static displacements and 0.21% for natural frequencies. The SBFEM approach accurately captures hydrodynamic pressures and radiation damping, revealing peak pressures at the dam heel during resonance and demonstrating computational efficiency with significantly reduced nodal requirements compared to FEM. These findings enhance understanding of dam behavior under extreme loading. The proposed framework supports climate-adaptive design standards and integrated hydrological–structural modeling. By addressing the seismic safety of flood-control dams, this research contributes to the development of resilient urban water management systems capable of protecting metropolitan areas from compound climatic and seismic extremes. Full article

(This article belongs to the Special Issue Advances in Urban Hydrology and Stormwater Management)

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20 pages, 6835 KiB

Open AccessArticle

Spatiotemporal Changes in Extreme Temperature and Associated Large-Scale Climate Driving Forces in Chongqing

by Chujing Wang, Yuefeng Wang, Chaogui Lei, Sitong Wei, Xingying Huang, Zhenghui Zhu and Shuqiong Zhou

Hydrology 2025, 12(8), 208; https://doi.org/10.3390/hydrology12080208 - 7 Aug 2025

Abstract

Due to global warming, extreme temperature events have become increasingly prevalent, posing significant threats to both socioeconomic development and human safety. While previous studies have extensively examined the influence of individual climatic circulation systems on extreme temperature, the combined effects of multiple concurrent [...] Read more.

Due to global warming, extreme temperature events have become increasingly prevalent, posing significant threats to both socioeconomic development and human safety. While previous studies have extensively examined the influence of individual climatic circulation systems on extreme temperature, the combined effects of multiple concurrent circulation patterns remain poorly understood. Using daily temperature data from 29 meteorological stations in Chongqing (1960–2019), this study employs linear trend analysis, correlation analysis, and random forest (RF) models to analyze spatiotemporal variations in the intensity and frequency of extreme temperature. We selected 21 climate indicators from three categories—atmospheric circulation, sea surface temperature (SST), and sea-level pressure (SLP)—to identify the primary drivers of extreme temperatures and quantify their respective contributions. The key findings are as follows: (1) All extreme intensity indices exhibited an increasing trend, with the TXx (annual maximum daily maximum temperature) showing the higher trend (0.03 °C/year). The northeastern region experienced the most pronounced increases. (2) Frequency indices also displayed an upward trend. This was particularly evident for the TD35 (number of days with maximum temperature ≥35 °C), which increased at an average rate of 0.16 days/year, most notably in the northeast. (3) The Western Pacific Subtropical High Ridge Position Index (GX) and Asia Polar Vortex Area Index (APV) were the dominant climate factors driving intensity indices, with cumulative contributions of 26.0% to 33.4%, while the Western Pacific Warm Pool Strength Index (WPWPS), Asia Polar Vortex Area Index (APV), North Atlantic Subtropical High Intensity Index (NASH), and Indian Ocean Warm Pool Strength Index (IOWP) were the dominant climate factors influencing frequency indices, with cumulative contributions of 46.4 to 49.5%. The explanatory power of these indices varies spatially across stations, and the RF model effectively identifies key circulation factors at each station. In the future, more attention should be paid to urban planning adaptations, particularly green infrastructure and land use optimization, along with targeted heat mitigation strategies, such as early warning systems and public health interventions, to strengthen urban resilience against escalating extreme temperatures. Full article

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30 pages, 5262 KiB

Open AccessArticle

Alternative Hydraulic Modeling Method Based on Recurrent Neural Networks: From HEC-RAS to AI

by Andrei Mihai Rugină

Hydrology 2025, 12(8), 207; https://doi.org/10.3390/hydrology12080207 - 6 Aug 2025

Abstract

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural [...] Read more.

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural architectures were analyzed as follows: S-RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU. The input data for the neural networks were derived from 2D hydraulic simulations conducted using HEC-RAS software, which provided the necessary training data for the models. It should be mentioned that the input data for the hydraulic model are synthetic hydrographs, derived from the statistical processing of recorded floods. Performance evaluation was based on standard metrics such as NSE,

R^{2}

MSE, and RMSE. The results indicate that all studied networks performed well, with NSE and

R^{2}

values close to 1, thus validating their capacity to reproduce complex hydrological dynamics. Overall, all models yielded satisfactory results, making them useful tools particularly the GRU and Bi-GRU architectures, which showed the most balanced behavior, delivering low errors and high stability in predicting peak discharge, water level, and flood wave volume. The GRU and Bi-GRU networks yielded the best performance, with RMSE values below 1.45, MAE under 0.3, and volume errors typically under 3%. On the other hand, LSTM architecture exhibited the most significant instability and errors, especially in estimating the flood wave volume, often having errors exceeding 9% in some sections. The study concludes by identifying several limitations, including the heavy reliance on synthetic data and its local applicability, while also proposing solutions for future analyses, such as the integration of real-world data and the expansion of the methodology to diverse river basins thus providing greater significance to RNN models. The final conclusions highlight that RNNs are powerful tools in flood risk management, contributing to the development of fast and efficient early warning systems for extreme hydrological and meteorological events. Full article

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29 pages, 10015 KiB

Open AccessArticle

Analysis of Spatiotemporal Characteristics of Global TCWV and AI Hybrid Model Prediction

by Longhao Xu, Kebiao Mao, Zhonghua Guo, Jiancheng Shi, Sayed M. Bateni and Zijin Yuan

Hydrology 2025, 12(8), 206; https://doi.org/10.3390/hydrology12080206 - 6 Aug 2025

Abstract

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall [...] Read more.

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall test, sliding change-point detection, wavelet transform, pixel-scale trend estimation, and linear regression to analyze the spatiotemporal dynamics of global TCWV from 1959 to 2023 and its impacts on agricultural systems, surpassing the limitations of single-method approaches. Results reveal a global TCWV increase of 0.0168 kg/m²/year from 1959–2023, with a pivotal shift in 2002 amplifying changes, notably in tropical regions (e.g., Amazon, Congo Basins, Southeast Asia) where cumulative increases exceeded 2 kg/m² since 2000, while mid-to-high latitudes remained stable and polar regions showed minimal content. These dynamics escalate weather risks, impacting sustainable agricultural management with irrigation and crop adaptation. To enhance prediction accuracy, we propose a novel hybrid model combining wavelet transform with LSTM, TCN, and GRU deep learning models, substantially improving multidimensional feature extraction and nonstationary trend capture. Comparative analysis shows that WT-TCN performs the best (MAE = 0.170, R² = 0.953), demonstrating its potential for addressing climate change uncertainties. These findings provide valuable applications for precision agriculture, sustainable water resource management, and disaster early warning. Full article

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16 pages, 2576 KiB

Open AccessArticle

Modeling and Spatiotemporal Analysis of Actual Evapotranspiration in a Desert Steppe Based on SEBS

by Yanlin Feng, Lixia Wang, Chunwei Liu, Baozhong Zhang, Jun Wang, Pei Zhang and Ranghui Wang

Hydrology 2025, 12(8), 205; https://doi.org/10.3390/hydrology12080205 - 6 Aug 2025

Abstract

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based [...] Read more.

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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28 pages, 15658 KiB

Open AccessArticle

Unifying Flood-Risk Communication: Empowering Community Leaders Through AI-Enhanced, Contextualized Storytelling

by Michal Zajac, Connor Kulawiak, Shenglin Li, Caleb Erickson, Nathan Hubbell and Jiaqi Gong

Hydrology 2025, 12(8), 204; https://doi.org/10.3390/hydrology12080204 - 4 Aug 2025

Abstract

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood [...] Read more.

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article

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20 pages, 4109 KiB

Open AccessArticle

Quantifying Baseflow with Radon, H and O Isotopes and Field Parameters in the Urbanized Catchment of the Little Jukskei River, Johannesburg

by Khutjo Diphofe, Roger Diamond and Francois Kotze

Hydrology 2025, 12(8), 203; https://doi.org/10.3390/hydrology12080203 - 2 Aug 2025

Abstract

Understanding groundwater and surface water interaction is critical for managing water resources, particularly in water-stressed and rapidly urbanizing areas, such as many parts of Africa. A survey was conducted of borehole, spring, seep and river water radon, δ²H, δ¹⁸O [...] Read more.

Understanding groundwater and surface water interaction is critical for managing water resources, particularly in water-stressed and rapidly urbanizing areas, such as many parts of Africa. A survey was conducted of borehole, spring, seep and river water radon, δ²H, δ¹⁸O and field parameters in the Jukskei River catchment, Johannesburg. Average values of electrical conductivity (EC) were 274 and 411 μS·cm⁻¹ for groundwater and surface water, and similarly for radon, 37,000 and 1100 Bq·m⁻³, with a groundwater high of 196,000 Bq·m⁻³ associated with a structural lineament. High radon was a good indicator of baseflow, highest at the end of the rainy season (March) and lowest at the end of the dry season (September), with the FINIFLUX model computing groundwater inflow as 2.5–4.7 L·m⁻¹s⁻¹. High EC was a poorer indicator of baseflow, also considering the possibility of wastewater with high EC, typical in urban areas. Groundwater δ²H and δ¹⁸O values are spread widely, suggesting recharge from both normal and unusual rainfall periods. A slight shift from the local meteoric water line indicates light evaporation during recharge. Surface water δ²H and δ¹⁸O is clustered, pointing to regular groundwater input along the stream, supporting the findings from radon. Given the importance of groundwater, further study using the same parameters or additional analytes is advisable in the urban area of Johannesburg or other cities. Full article

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20 pages, 7673 KiB

Open AccessArticle

Impact of Elevation and Hydrography Data on Modeled Flood Map Accuracy Using ARC and Curve2Flood

by Taylor James Miskin, L. Ricardo Rosas, Riley C. Hales, E. James Nelson, Michael L. Follum, Joseph L. Gutenson, Gustavious P. Williams and Norman L. Jones

Hydrology 2025, 12(8), 202; https://doi.org/10.3390/hydrology12080202 - 1 Aug 2025

Abstract

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These [...] Read more.

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

(This article belongs to the Special Issue Advances in Flood Studies: Enhancing Data Collection, Rating Curves, and Hydrological Analyses)

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17 pages, 4148 KiB

Open AccessArticle

Disastrous Effects of Hurricane Helene in the Southern Appalachian Mountains Including a Review of Mechanisms Producing Extreme Rainfall

by Jeff Callaghan

Hydrology 2025, 12(8), 201; https://doi.org/10.3390/hydrology12080201 - 31 Jul 2025

Abstract

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well [...] Read more.

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Ashville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Ashville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United Stares from Helene. This is of extreme concern and should be investigated in detail as the public expect the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, run off from such rainfall events increases, which causes greater devastation. Full article

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28 pages, 2566 KiB

Open AccessArticle

Simulating Effectiveness of Low Impact Development (LID) for Different Building Densities in the Face of Climate Change Using a Hydrologic-Hydraulic Model (SWMM₅)

by Helene Schmelzing and Britta Schmalz

Hydrology 2025, 12(8), 200; https://doi.org/10.3390/hydrology12080200 - 31 Jul 2025

Abstract

To date, few studies have been published for cities in Germany that take into account climate change and changing hydrologic patterns due to increases in building density. This study investigates the efficiency of LID for past and future climate in the polycentric agglomeration [...] Read more.

To date, few studies have been published for cities in Germany that take into account climate change and changing hydrologic patterns due to increases in building density. This study investigates the efficiency of LID for past and future climate in the polycentric agglomeration area Frankfurt, Main (Central Germany) using observed and projected climate (model) data for a standard reference period (1961–1990) and a high emission scenario (RCP 8.5) as well as a climate protection scenario (RCP 2.6), under 40 to 75 percent building density. LID elements included green roofs, permeable pavement and bioretention cells. SWMM₅ was used as model for simulation purposes. A holistic evaluation of simulation results showed that effectiveness increases incrementally with LID implementation percentage and inverse to building density if implemented onto at least 50 percent of available impervious area. Building density had a higher adverse effect on LID efficiency than climate change. The results contribute to the understanding of localized effects of climate change and the implementation of adaption strategies to that end. The results of this study can be helpful for the scientific community regarding future investigations of LID implementation efficiency in dense residential areas and used by local governments to provide suggestions for urban water balance revaluation. Full article

(This article belongs to the Topic Water Management in the Age of Climate Change)

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26 pages, 3711 KiB

Open AccessArticle

Probability Characteristics of High and Low Flows in Slovakia: A Comprehensive Hydrological Assessment

by Pavla Pekárová, Veronika Bačová Mitková and Dana Halmová

Hydrology 2025, 12(8), 199; https://doi.org/10.3390/hydrology12080199 - 31 Jul 2025

Abstract

Frequency analysis is essential for designing hydraulic structures and managing water resources, as it helps assess hydrological extremes. However, changes in river basins can impact their accuracy, complicating the link between discharge and return periods. This study aims to comprehensively assess the probability [...] Read more.

Frequency analysis is essential for designing hydraulic structures and managing water resources, as it helps assess hydrological extremes. However, changes in river basins can impact their accuracy, complicating the link between discharge and return periods. This study aims to comprehensively assess the probability characteristics of long-term M-day maximum/minimum discharges in the Carpathian region of Slovakia. We analyze the long-term data from 26 gauging stations covering 90 years of observation. Slovak rivers show considerable intra-annual variability, especially between the summer–autumn (SA) and winter–spring (WS) seasons. To allow consistent comparisons, we apply a uniform methodology to estimate T-year daily maximum and minimum specific discharges over durations of 1 and 7 days for both seasons. Our findings indicate that 1-day maximum specific discharges are generally higher during the SA season compared to the WS season. The 7-day minimum specific discharges are lower during the WS season compared to the SA season. Slovakia’s diverse orographic and climatic conditions cause significant spatial variability in extreme discharges. However, the estimated T-year 7-day minimum and 1-day maximum specific discharges, based on the mean specific discharge and the altitude of the water gauge, exhibit certain nonlinear dependences. These relationships could support the indirect estimation of T-year M-day discharges in regions with similar runoff characteristics. Full article

(This article belongs to the Section Water Resources and Risk Management)

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27 pages, 4619 KiB

Open AccessArticle

Assessing the Impact of Assimilated Remote Sensing Retrievals of Precipitation on Nowcasting a Rainfall Event in Attica, Greece

by Aikaterini Pappa, John Kalogiros, Maria Tombrou, Christos Spyrou, Marios N. Anagnostou, George Varlas, Christine Kalogeri and Petros Katsafados

Hydrology 2025, 12(8), 198; https://doi.org/10.3390/hydrology12080198 - 28 Jul 2025

Abstract

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these [...] Read more.

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these challenges by implementing the Local Analysis and Prediction System (LAPS) enhanced with a forward advection nowcasting module, integrating multiple remote sensing rainfall datasets. Specifically, we combine weather radar data with three different satellite-derived rainfall products (H-SAF, GPM, and TRMM) to assess their impact on nowcasting performance for a rainfall event in Attica, Greece (29–30 September 2018). The results demonstrate that combining high-resolution radar data with the broader coverage and high temporal frequency of satellite retrievals, particularly H-SAF, leads to more accurate predictions with lower uncertainty. The assimilation of H-SAF with radar rainfall retrievals (HX experiment) substantially improved forecast skill, reducing the unbiased Root Mean Square Error by almost 60% compared to the control experiment for the 60 min rainfall nowcast and 55% for the 90 min rainfall nowcast. This work validates the effectiveness of the specific LAPS/advection configuration and underscores the importance of multi-source data assimilation for weather prediction. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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21 pages, 4181 KiB

Open AccessArticle

Addressing Volatility and Nonlinearity in Discharge Modeling: ARIMA-iGARCH for Short-Term Hydrological Time Series Simulation

by Mahshid Khazaeiathar and Britta Schmalz

Hydrology 2025, 12(8), 197; https://doi.org/10.3390/hydrology12080197 - 27 Jul 2025

Abstract

Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes [...] Read more.

Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R² values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article

(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)

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11 pages, 15673 KiB

Open AccessArticle

Automating GIS-Based Cloudburst Risk Mapping Using Generative AI: A Framework for Scalable Hydrological Analysis

by Alexander Adiyasa, Andrea Niccolò Mantegna and Irma Kveladze

Hydrology 2025, 12(8), 196; https://doi.org/10.3390/hydrology12080196 - 23 Jul 2025

Abstract

Accurate dynamic hydrological models are often too complex and costly for the rapid, broad-scale screening necessitated for proactive land-use planning against increasing cloudburst risks. This paper demonstrates the use of GPT-4 to develop a GUI-based Python 3.13.2 application for geospatial flood risk assessments. [...] Read more.

Accurate dynamic hydrological models are often too complex and costly for the rapid, broad-scale screening necessitated for proactive land-use planning against increasing cloudburst risks. This paper demonstrates the use of GPT-4 to develop a GUI-based Python 3.13.2 application for geospatial flood risk assessments. The study used instructive prompt techniques to script a traditional stream and catchment delineation methodology, further embedding it with a custom GUI. The resulting application demonstrates high performance, processing a 29.63 km² catchment at a 1 m resolution in 30.31 s, and successfully identifying the main upstream contributing areas and flow paths for a specified area of interest. While its accuracy is limited by terrain data artifacts causing stream breaks, this study demonstrates how human–AI collaboration, with the LLM acting as a coding assistant guided by domain expertise, can empower domain experts and facilitate the development of advanced GIS-based decision-support systems. Full article

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15 pages, 2467 KiB

Open AccessArticle

Definition of Groundwater Management Zones for a Fissured Karst Aquifer in Semi-Arid Northeastern Brazil

by Hailton Mello da Silva, Luiz Rogério Bastos Leal, Cezar Augusto Teixeira Falcão Filho, Thiago dos Santos Gonçalves and Harald Klammler

Hydrology 2025, 12(8), 195; https://doi.org/10.3390/hydrology12080195 - 23 Jul 2025

Abstract

The objective of this study is to define groundwater management zones for a complex deformed and fissured Precambrian karst aquifer, which underlies one of the most important agricultural areas in the semi-arid region of Irecê, Bahia, Brazil. It is an unconfined aquifer, hundreds [...] Read more.

The objective of this study is to define groundwater management zones for a complex deformed and fissured Precambrian karst aquifer, which underlies one of the most important agricultural areas in the semi-arid region of Irecê, Bahia, Brazil. It is an unconfined aquifer, hundreds of meters thick, resulting from a large sequence of carbonates piled up by thrust faults during tectonic plate collisions. Groundwater recharge and flow in this aquifer are greatly influenced by karst features, through the high density of sinkholes and vertical wells. Over the past four decades, population and agricultural activities have increased in the region, resulting in unsustainable groundwater withdrawal and, at the same time, water quality degradation. Therefore, it is important to develop legal and environmental management strategies. This work proposes the division of the karst area into three well-defined management zones by mapping karst structures, land use, and urban occupation, as well as the concentrations of chloride and nitrate in the region’s groundwater. Zone 1 in the north possesses the lowest levels of karstification, anthropization, and contamination, while zone 2 in the central region has the highest levels and zone 3 in the south ranging in-between (except for stronger karstification). The delimitation of management zones will contribute to the development and implementation of optimized zone-specific groundwater preservation and restoration strategies. Full article

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28 pages, 12894 KiB

Open AccessFeature PaperArticle

Evolution of Rainfall Characteristics in Catalonia, Spain, Using a Moving-Window Approach (1950–2022)

by Carina Serra, María del Carmen Casas-Castillo, Raül Rodríguez-Solà and Cristina Periago

Hydrology 2025, 12(7), 194; https://doi.org/10.3390/hydrology12070194 - 19 Jul 2025

Abstract

A comprehensive analysis of the evolution of rainfall characteristics in Catalonia, NE Spain, was conducted using monthly data from 72 rain gauges over the period 1950–2022. A moving-window approach was applied at annual, seasonal, and monthly scales, calculating mean values, coefficients of variation [...] Read more.

A comprehensive analysis of the evolution of rainfall characteristics in Catalonia, NE Spain, was conducted using monthly data from 72 rain gauges over the period 1950–2022. A moving-window approach was applied at annual, seasonal, and monthly scales, calculating mean values, coefficients of variation (CV), and trends across 43 overlapping 31-year periods. To assess trends in these moving statistics, a modified Mann–Kendall test was applied to both the 31-year means and CVs. Results revealed a significant 10% decrease in annual rainfall, with summer showing the most pronounced decline, as nearly 90% of stations exhibited negative trends, while the CV showed negative trends in coastal areas and mostly positive trends inland. At the monthly scale, February, March, June, August, and December exhibited negative trends at more than 50% of stations, with rainfall reductions ranging from 20% to 30%. Additionally, the temporal evolution of Mann–Kendall trend coefficients within each 31-year moving window displayed a fourth-degree polynomial pattern, with a periodicity of 30–35 years at annual and seasonal scales, and for some months. Finally, at the annual scale and in two centennial series, the 80-year oscillations found were inversely correlated with the large-scale climate indices North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO). Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)

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