Hydrology

26 pages, 2806 KB

Open AccessArticle

Towards a Near-Real-Time Water Stress Monitoring System in Tropical Heterogeneous Landscapes Using Remote Sensing Data

by Abdul Holik, Wei Tian, Aris Psilovikos and Mohamed Elhag

Hydrology 2025, 12(12), 325; https://doi.org/10.3390/hydrology12120325 - 10 Dec 2025

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), [...] Read more.

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI), were analyzed across three contrasting agricultural systems: paddy, sugarcane, and rubber, revealing distinct phenological and water stress dynamics. Radar-derived structural indices captured patterns of biomass accumulation and canopy development, with VV/VH values ranging from 4.2 to 12.3 in paddy and 5.4 to 6.0 in rubber. In parallel, optical moisture indices detected crop physiological stress; for instance, NDMI dropped from 0.26 to 0.06 during drought in sugarcane. Cross-index analyses demonstrated strong complementarity; synchronized VV/VH and RDI peaks characterized paddy inundation, whereas lagged NDMI–VV/VH responses captured stress-induced defoliation in rubber trees. Temporal profiling established crop-specific diagnostic signatures, with DpRVI peaking at 0.75 in paddy, gradual RDI decline in sugarcane, and NDMI values of 0.2–0.3 in rubber. The framework provides spatially explicit, temporally continuous, and cost-effective monitoring to support irrigation, drought early warning, and agricultural planning. Multi-year validation and field-based calibration are recommended for operational implementation. Full article

(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)

► Show Figures

Figure 1

14 pages, 5402 KB

Open AccessArticle

Forecasting River Ice Breakup and Ice Jam Flooding

by Hung Tao Shen and Fengbin Huang

Hydrology 2025, 12(12), 324; https://doi.org/10.3390/hydrology12120324 - 10 Dec 2025

Abstract

Mechanical breakup of river ice cover and associated ice jam flooding is a major concern for riverine communities in cold regions. The ability to forecast breakup ice jams is essential for river ice management. Numerous studies on forecasting breakup ice jams have been [...] Read more.

Mechanical breakup of river ice cover and associated ice jam flooding is a major concern for riverine communities in cold regions. The ability to forecast breakup ice jams is essential for river ice management. Numerous studies on forecasting breakup ice jams have been conducted. This study reviews existing breakup forecasting methods, including data-driven and machine learning techniques, and discusses their shortcomings and possible improvements in selecting input parameters. Since the weather during breakup time often changes rapidly, forecasting in a Nowcasting mode to assess the risk of mechanical breakup and ice jam flooding is necessary to issue flood warnings and support emergency operations. A physically based method for rapidly forecasting ice cover breakup and ice jam flooding is developed, which also provides information to improve existing forecasting methods. Full article

► Show Figures

Figure 1

36 pages, 4297 KB

Open AccessReview

Data Assimilation in Hydrological Models: Methods, Challenges and Emerging Trends

by Xiaozhe Yuan, Geng Niu, Junxian Yin and Yulei Xie

Hydrology 2025, 12(12), 323; https://doi.org/10.3390/hydrology12120323 - 9 Dec 2025

Abstract

The integration of multi-source data represents a defining trend in hydrological science, while the comprehensive quantification and characterization of inherent uncertainties in hydrological model prediction remains imperative. Data assimilation (DA) techniques offer a rigorous framework for integrating multi-source observational data with model simulations [...] Read more.

The integration of multi-source data represents a defining trend in hydrological science, while the comprehensive quantification and characterization of inherent uncertainties in hydrological model prediction remains imperative. Data assimilation (DA) techniques offer a rigorous framework for integrating multi-source observational data with model simulations through systematic uncertainty characterization, thereby enhancing predictive accuracy while providing quantitative uncertainty estimates. This study systematically synthesizes and extracts the research hotspots and cutting-edge trends of DA within the hydrology domain. Specifically, from the perspectives of model structure, parameters, and states, it categorizes the development of data assimilation techniques in hydrology into system identification, parameter estimation, and state estimation. The research identifies several key challenges confronting the field of hydrological DA, including inherent nonlinear characteristics of hydrological processes, insufficient spatial coverage and limited availability of observational data, necessity for substantial modifications to existing hydrological models for DA compatibility, difficulties in quantifying errors within raw datasets, and computational complexity arising from high-dimensional state spaces during assimilation. Finally, using the Kalman filter as an illustrative example, the study demonstrates the concrete application of DA. It is proposed that the integration of deep learning with DA, coupled with the joint estimation of parameters and states, represents the promising and breakthrough directions for the future development of DA methodologies in hydrological research. Full article

► Show Figures

Figure 1

34 pages, 4552 KB

Open AccessArticle

Dynamic Graph Transformer with Spatio-Temporal Attention for Streamflow Forecasting

by Bo Li, Qingping Li, Xinzhi Zhou, Mingjiang Deng and Hongbo Ling

Hydrology 2025, 12(12), 322; https://doi.org/10.3390/hydrology12120322 - 8 Dec 2025

Abstract

Accurate streamflow forecasting is crucial for water resources management and flood mitigation, yet it remains challenging due to the complex dynamics of hydrological systems. Conventional data-driven approaches often struggle to effectively capture spatio-temporal evolution characteristics, particularly the dynamic interdependencies among streamflow gauges. This [...] Read more.

Accurate streamflow forecasting is crucial for water resources management and flood mitigation, yet it remains challenging due to the complex dynamics of hydrological systems. Conventional data-driven approaches often struggle to effectively capture spatio-temporal evolution characteristics, particularly the dynamic interdependencies among streamflow gauges. This study proposes a novel deep learning architecture, termed DynaSTG-Former. It employs a multi-channel dynamic graph constructor to adaptively integrate three spatial dependency patterns: physical topology, statistical correlation, and trend similarity. A dual-stream temporal predictor is designed to collaboratively model long-range dependencies and local transient features. In an empirical study within the Delaware River Basin, the model demonstrated exceptional performance in multi-step-ahead forecasting (12-, 36-, and 72 h). It achieved basin-scale Kling–Gupta Efficiency (KGE) values of 0.961, 0.956, and 0.855, significantly outperforming baseline models such as LSTM, GRU, and Transformer. Ablation studies confirmed the core contribution of the dynamic graph module, with the Pearson correlation graph playing a dominant role in error reduction. The results indicate that DynaSTG-Former effectively enhances the accuracy and stability of streamflow forecasts and demonstrates its strong robustness at the basin scale. It thus provides a reliable tool for precision water management. Full article

► Show Figures

Figure 1

31 pages, 23420 KB

Open AccessArticle

Grand Teton National Park Trophic State Evolution at 33 Locations in 29 Lakes over Three Decades: Field Data and Analysis

by A. Woodruff Miller, Pitamber Wagle, Amin Aghababaei, Abin Raj Chapagain, Yubin Baaniya, Peter D. Oldham, Samuel J. Oldham, Tyler Peterson, Lyle Prince, Rachel Huber Magoffin, Xueyi Li, Taylor Miskin, Kaylee B. Tanner, Anna C. Cardall, Norman L. Jones and Gustavious P. Williams

Hydrology 2025, 12(12), 321; https://doi.org/10.3390/hydrology12120321 - 6 Dec 2025

Abstract

We present a 30-year analysis of water quality trends in Grand Teton National Park, based on 715 sampling events we collected at 33 locations across 29 lakes from 1995 to 2025. Our dataset includes Secchi depth, chlorophyll-a, and total phosphorus, collected seasonally from [...] Read more.

We present a 30-year analysis of water quality trends in Grand Teton National Park, based on 715 sampling events we collected at 33 locations across 29 lakes from 1995 to 2025. Our dataset includes Secchi depth, chlorophyll-a, and total phosphorus, collected seasonally from both in-lake and inlet sites. We classified lake trophic states using the Carlson Trophic State Index (CTSI) and the Vollenweider (VW) and Larsen–Mercier (LM) models. Most lakes remain mesotrophic (CTSI 38–54), with larger lakes such as Jackson and Phelps showing lower total phosphorus, while smaller lakes like Christian Pond and Cygnet Pond exhibit higher chlorophyll-a. High-elevation lakes generally have reduced nutrient concentrations. Seasonal effects are pronounced, with late summer and fall samples—especially at Swan Lake and Two Ocean Lake—showing increased chlorophyll-a. Trend analysis using the Mann–Kendall test identified statistically significant decreases in chlorophyll-a for six lakes and in total phosphorus for fifteen lakes; no lakes showed significant increases in any parameter. Four lakes—Christian Pond, Swan Lake, Two Ocean Lake, and Oxbow Bend—demonstrated consistent improvements across all measured indicators. The magnitude of these declines was modest, suggesting gradual oligotrophication rather than widespread eutrophication. Our comparison of trophic state models highlights that VW and LM often assign higher trophic classifications than CTSI. This study provides a robust baseline for understanding the resilience of high-elevation lakes in Grand Teton National Park. Our unique dataset, collected from remote and often barely accessible sites, is publicly available to support future research and management. Continued monitoring is essential to detect potential impacts of climate change and human activity, ensuring the preservation of these sensitive aquatic ecosystems. Full article

(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)

► Show Figures

Figure 1

17 pages, 649 KB

Open AccessReview

Advances and Challenges in Dew Research on Land Surface: A Review

by Hongyuan Li, Chuntan Han, Yong Yang and Rensheng Chen

Hydrology 2025, 12(12), 320; https://doi.org/10.3390/hydrology12120320 - 5 Dec 2025

Abstract

Dew, a key component of Non-Rainfall Water Inputs (NRWIs), plays a disproportionately significant role in land–atmosphere interactions. This review synthesizes advances in understanding its ecological, hydrological, and environmental effects, quantification methods, and spatiotemporal variations. A key finding is the regional dichotomy of dew [...] Read more.

Dew, a key component of Non-Rainfall Water Inputs (NRWIs), plays a disproportionately significant role in land–atmosphere interactions. This review synthesizes advances in understanding its ecological, hydrological, and environmental effects, quantification methods, and spatiotemporal variations. A key finding is the regional dichotomy of dew effects: in arid regions, it is a crucial hydrological source, whereas in humid/alpine regions, its energy-balance regulation via latent heat release often outweighs its hydrological contribution. Significant challenges persist, including methodological inconsistencies, an overreliance on point-scale data from arid zones, and an underappreciation of dew’s energy impacts, particularly in cold regions. Recent studies suggest a general declining trend in dew frequency and amount in many arid regions, which could exacerbate water stress for dependent ecosystems. However, regional heterogeneities and interactions with other NRWIs remain poorly constrained. Future research must overcome observational bottlenecks, deepen energy–water coupling studies, quantify climate change impacts, expand research to underrepresented regions, and integrate multi-method approaches to improve model predictability, thereby supporting ecosystem resilience and water security under global change. Full article

(This article belongs to the Section Ecohydrology)

► Show Figures

Figure 1

23 pages, 3017 KB

Open AccessArticle

Water Resource Allocation Considering the Effects of Emergency Supply Augmentation Costs and Water Use Compression Losses Under Extreme Drought Conditions

by Chentao He, Xi Guo and Zening Wu

Hydrology 2025, 12(12), 319; https://doi.org/10.3390/hydrology12120319 - 4 Dec 2025

Abstract

Extreme drought intensifies the complexity of the water resource allocation system, and unreasonable water distribution exacerbates drought losses. Drought mitigation measures such as emergency water supply augmentation and water use compression incur additional costs or losses, thereby compromising the accuracy of water allocation [...] Read more.

Extreme drought intensifies the complexity of the water resource allocation system, and unreasonable water distribution exacerbates drought losses. Drought mitigation measures such as emergency water supply augmentation and water use compression incur additional costs or losses, thereby compromising the accuracy of water allocation outcomes. To address the insufficient consideration of the impacts of emergency water supply augmentation and water use compression measures under extreme drought conditions in current research, this study employs emergy theory to systematically identify and quantify the emergency water supply augmentation costs and water use compression losses. A dual-objective water resource allocation model was constructed under extreme drought conditions by taking the minimization of the sum of the emergency water supply augmentation costs and water use compression losses as the comprehensive loss objective, and the minimization of the total water scarcity as the water use guarantee objective. The model was subsequently transformed into a single-objective optimization problem for solution. The allocation model was applied to the typical severe drought scenario in Chuxiong Prefecture of Yunnan Province in 2011. The results demonstrate that the scheme implementing both measures reduced comprehensive losses by 4.97 × 10¹⁹ sej and total water shortage by 7.02 × 10⁶ m³ compared to the scheme excluding these measures. The water resource allocation model considering emergency water supply augmentation costs and water use compression losses can effectively mitigate the drought impact in the study area. Full article

(This article belongs to the Special Issue Sustainable Water Management in the Face of Drastic Climate Change)

► Show Figures

Graphical abstract

17 pages, 3294 KB

Open AccessArticle

Detecting 3D Anomalies in Soil Water from Saline-Alkali Land of Yellow River Delta Using Sampling Data

by Zhoushun Han, Xin Fu, Haoran Zhang, Yang Li, Lehang Tang, Hengcai Zhang and Zhenghe Xu

Hydrology 2025, 12(12), 318; https://doi.org/10.3390/hydrology12120318 - 1 Dec 2025

Abstract

Understanding soil water in the saline-alkali lands is crucial for sustainable agriculture and ecological restoration. Existing studies have largely focused on macroscopic distribution and associated interpolation techniques, which complicates the precise identification of localized anomalous regions. To address this limitation, this study proposes [...] Read more.

Understanding soil water in the saline-alkali lands is crucial for sustainable agriculture and ecological restoration. Existing studies have largely focused on macroscopic distribution and associated interpolation techniques, which complicates the precise identification of localized anomalous regions. To address this limitation, this study proposes a novel three-dimensional detection method for localized soil water anomalies (3D-SWLA). Utilizing soil water sampling data, a comprehensive three-dimensional soil water cube is constructed through 3D Empirical Bayesian Kriging (3D EBK). We introduce the Soil Water Local Anomaly Index (SWLAI) and apply a second-order difference method to effectively identify and filter anomalous voxels. Then, the 3D Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster Soil Water Anomalous Voxels (SWAVs), thereby delineating three-dimensional Local Anomalous Soil Water Areas (LASWAs) with precision and robustness. A series of experiments were conducted in Kenli to validate the proposed methodology. The results reveal that 3D-SWLA successfully identified a total of 8 Local Anomalous Soil Water Areas (LASWAs), four of which—classified as large-scale anomalies (area > 1.0 km²)—are predominantly concentrated in the northeastern coastal zone and the southern salt fields. The largest among them, LASWA-1, spans 1.8 km² with a vertical depth ranging from 0 to 35 cm and an average soil water content of 0.36. Another significant anomaly, LASWA-8, covers 1.5 km², extends to a depth of 0–60 cm, and exhibits a higher average water content of 0.42, reflecting distinct hydrological dynamics in these regions. Additionally, 4 smaller LASWAs (area < 1.0 km²) are spatially distributed along the northeastern irrigation channels, indicating localized moisture accumulation likely influenced by agricultural water management. Full article

► Show Figures

Figure 1

24 pages, 16248 KB

Open AccessArticle

Drivers and Future Risks of Groundwater Projection in Tangshan, China: Integrating SHAP, Geographically Weighted Regression, and Climate–Land-Use Scenarios

by Arifullah, Yicheng Wang, Hejia Wang and Jia Liu

Hydrology 2025, 12(12), 317; https://doi.org/10.3390/hydrology12120317 - 30 Nov 2025

Abstract

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local [...] Read more.

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local Indicators of Spatial Association or LISA), and scenario-based recharge forecasting to evaluate the spatial drivers and patterns of groundwater stress and project potential future risks. Using spatial groundwater table data from 2022 and key environmental and anthropogenic variables such as evapotranspiration (ET), population, temperature, precipitation, and land use and land cover changes, an XGBoost (Extreme Gradient Boosting) regression model was trained to capture nonlinear spatial patterns. SHAP analysis found that ET and population density are prominent contributors to groundwater depletion in agricultural and urban zones. To capture spatial heterogeneity, GWR was utilized to estimate localized coefficients and construct a Vulnerability and Resilience Index (VRI) from normalized coefficients and residuals. LISA validated vulnerability zones and revealed transitional stress regions. Future risks are also projected using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data and land-use data to run recharge modeling from 2023 to 2049 for both representative concentration pathway (RCP) 4.5 and RCP 8.5. Results show that RCP 8.5 demonstrates highly unstable recharge with frequent negative episodes (ET > P), while RCP 4.5 shows relatively stable patterns of groundwater table. Furthermore, coupled with urban and agricultural expansion, RCP 8.5 intensifies depletion risks. This combined framework provides analytical understandings of spatial driver patterns and scenario-based risk assessments under climate and land use change. The findings of the study recommend priority zones for intervention and underline the importance of adaptive, scenario-sensitive groundwater governance in semi-arid, urbanizing regions. Full article

► Show Figures

Figure 1

20 pages, 12557 KB

Open AccessArticle

The Atmospheric Water Cycle over South America as Seen in the New Generation of Global Reanalyses

by Mário Francisco Leal de Quadro, Dirceu Luís Herdies, Ernesto Hugo Berbery, Caroline Bresciani, Fabrício Daniel dos Santos Silva, Helber Barros Gomes, Michel Nobre Muza, Cássio Aurélio Suski and Diego Portalanza

Hydrology 2025, 12(12), 316; https://doi.org/10.3390/hydrology12120316 - 29 Nov 2025

Abstract

We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and [...] Read more.

We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and Subtropical South Atlantic Convergence Zone (TSACZ, SSACZ) and southeastern South America (SESA), Taylor-diagram skill metrics, and a vertically integrated moisture-budget residual as a proxy for closure. All products reproduce the large-scale spatial and seasonal patterns, but disagreements persist over the Andes and parts of the central/northern Amazon. Relative to CPC/MSWEP-V2, MERRA-2 exhibits the smallest precipitation biases and the highest correlations, followed by ERA5; CFSR/CFSv2 shows a warm-season wet bias. Moisture-budget residuals are smallest in MERRA-2, moderate in ERA5, and largest in CFSR/CFSv2, with clear regional and seasonal dependence. These results document improvements in the new generation of reanalyses while highlighting persistent challenges in gauge-sparse and complex-orography regions. For hydroclimate applications that depend on internally consistent P, E, moisture-flux convergence, and runoff, MERRA-2 provides the most coherent depiction among the three, whereas ERA5 is a strong alternative when higher spatial/temporal resolution or dynamical fields are needed and CFSR/CFSv2 should be applied with caution for warm-season precipitation and closure-sensitive analyses. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations: 2nd Edition)

► Show Figures

Figure 1

20 pages, 2522 KB

Open AccessArticle

The Estimation of Evapotranspiration Rates from Urban Green Infrastructure Using the Three-Temperatures Method

by Bruce Wickham, Simon De-Ville and Virginia Stovin

Hydrology 2025, 12(12), 315; https://doi.org/10.3390/hydrology12120315 - 27 Nov 2025

Abstract

The three-temperatures (3T) method is a robust approach to estimating evapotranspiration (ET), requiring relatively few measurable, physical parameters and an imitation surface, making it potentially suited for estimating ET from sustainable drainage systems (SuDS) and green infrastructure (GI) in urban environments. However, limited [...] Read more.

The three-temperatures (3T) method is a robust approach to estimating evapotranspiration (ET), requiring relatively few measurable, physical parameters and an imitation surface, making it potentially suited for estimating ET from sustainable drainage systems (SuDS) and green infrastructure (GI) in urban environments. However, limited 3T-ET data from SuDS and/or GI makes it difficult to assess the conditions that affect its accuracy. The purpose of this study was to determine whether reasonable ET estimates could be achieved using the 3T method with a plastic imitation surface for a small, homogenous vegetated surface. The 3T-ET estimates were produced at an hourly timestep and compared to reference ET (

E T_{o}

) derived using the Penman–Monteith equation. The 3T-ET estimates were consistently higher than

E T_{o}

(mean absolute error of 0.05 to 0.15 mm·h⁻¹), which may indicate systematic overestimation of ET or that the actual ET was greater than

E T_{o}

. Unrealistic 3T-ET estimates are produced when the air temperature and the imitation surface temperature converge, limiting the method’s application to between mid-morning and late afternoon. Further work to validate and refine the 3T method is required before it can be recommended for deployment in the field for spot-sampling ET rates from urban SuDS/GI. Full article

► Show Figures

Figure 1

29 pages, 48540 KB

Open AccessArticle

Morphostructural Controls Reflected in Drainage Patterns

by Raissa Eduarda da Silva Archanjo, Pablo César Serafim, Bruno César dos Santos, Vandoir Bourscheidt, Rodrigo Martins Moreira, Nelson Ferreira Fernandes, Paulo Henrique Souza, Ronaldo Luiz Mincato and Felipe Gomes Rubira

Hydrology 2025, 12(12), 314; https://doi.org/10.3390/hydrology12120314 - 26 Nov 2025

Abstract

The drainage network of the Upper Araguari River, Brazil, developed within an intraplate setting characterized by the Brasiliano structural inheritance, Mesozoic magmatism, and marked lithological contrasts. Although these factors strongly influence fluvial organization, gaps remain in how litho-structural controls modulate fluvial transience and [...] Read more.

The drainage network of the Upper Araguari River, Brazil, developed within an intraplate setting characterized by the Brasiliano structural inheritance, Mesozoic magmatism, and marked lithological contrasts. Although these factors strongly influence fluvial organization, gaps remain in how litho-structural controls modulate fluvial transience and divide stability in intraplate regions. We hypothesize that drainage systems constrained by structural controls and resistant lithologies exhibit higher k_sn values, larger χ offsets, greater knickpoint frequency, and less stable divides than systems developed on friable substrates. To test this hypothesis, we applied integrated morphometric metrics (χ parameter, normalized channel steepness index—k_sn, knickpoints, roughness concentration index—Rci, stream frequency—Sf, drainage density—Dd, and lineaments) across 23 sub-basins to assess how the litho-structural conditions influence the drainage patterns, the fluvial gradients, the equilibrium states, and the divide stability. We identified 57 knickpoints and high k_sn values concentrated in quartzitic and basaltic terrains and along fault zones. χ-plot offsets near quartzite–phyllite/schist contacts indicate transient fronts slowed by differential erodibility, whereas catchments developed on friable substrates respond more rapidly to perturbations. Trellis, rectangular, parallel, and radial drainage patterns exhibit greater instability, underscoring the integrated role of lithological contrasts and tectonic reactivations in modulating intraplate fluvial transience. Full article

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

► Show Figures

Figure 1

19 pages, 2459 KB

Open AccessArticle

Multivariate RVA Assessment of Hydrological Alterations: Huangshui River, Xining

by Wanqi Wang, Hao Wang, Feng Wang, Xiaohui Lei, Xiaoyan Wei and Kang Li

Hydrology 2025, 12(12), 313; https://doi.org/10.3390/hydrology12120313 - 26 Nov 2025

Abstract

Indicators of Hydrologic Alteration (IHA) are commonly screened with the Range of Variability Approach (RVA), which captures frequency shifts but can miss changes in central tendency, dispersion, distributional shape, and trend. We propose a Comprehensive Degree (CD) index that integrates RVA with these [...] Read more.

Indicators of Hydrologic Alteration (IHA) are commonly screened with the Range of Variability Approach (RVA), which captures frequency shifts but can miss changes in central tendency, dispersion, distributional shape, and trend. We propose a Comprehensive Degree (CD) index that integrates RVA with these four statistical dimensions and apply it to daily discharge at the Xining station on the Huangshui River (1954–2022). Using conventional RVA, the overall alteration was 61.16% (moderate). After integration, alteration increased by 7.59% to 68.75%, reclassifying the regime as high. Across 32 Indicators, 15 showed larger alteration and 12 moved up one class, whereas 17 decreased and 2 moved down. Distributional shape and trend dominated the signal, revealing strongly altered ecohydrological indicators—most notably low-pulse frequency/duration and 3-day minimum—and, additionally, flagging indicators that RVA downplays (e.g., April–August monthly flows) via high trend and distributional shape shifts. The framework addresses RVA’s frequency-only blind spots, is more robust to short-term or episodic fluctuations, and improves diagnostic precision and ecological interpretability. These results provide a decision-ready basis for adaptive environmental flow management in climatically sensitive, topographically complex plateau basins. Full article

► Show Figures

Graphical abstract

20 pages, 4437 KB

Open AccessArticle

Watershed Runoff Simulation and Prediction Based on BMA Coupled SWAT-LSTM Model

by Wenju Zhao, Yongwei Hao, Yongming Zhang, Haiying Yu and Xing Li

Hydrology 2025, 12(12), 312; https://doi.org/10.3390/hydrology12120312 - 24 Nov 2025

Abstract

In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) [...] Read more.

In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) model is coupled based on the Bayesian Model Averaging (BMA) method. The simulation accuracies of the optimized model are, respectively, compared with those of the SWAT (Soil and Water Assessment Tool) model and the SWAT-LSTM model. Taking the Zuli River Basin as an example, the optimal runoff prediction model for this basin is determined. Combining with future meteorological data, runoff predictions for the period from 2025 to 2030 are carried out. The findings indicate that the SWAT-LSTM-BMA coupled model is the optimal runoff prediction model for the Zuli River Basin. Compared with the SWAT model and the SWAT-LSTM model used alone, its simulation accuracy has been systematically improved. During the calibration period, R² increased by 8–12%, NSE increased by 9–13%, and MSE decreased by 14–30%. During the validation period, R² increased by 10–12%, NSE increased by 10–14%, and MSE decreased by 16–31%. Based on the model and the prediction of future climate data under multiple scenarios, the annual runoff of the basin will show a decreasing trend compared with the historical period between 2025 and 2030, with a decrease of 12–15%. The coupling framework proposed in this study effectively improves the accuracy of runoff prediction and provides a reliable theoretical foundation and technological assistance for revealing the evolution law of extreme hydrological events and the management of water resources in the basin. Full article

(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)

► Show Figures

Figure 1

16 pages, 2253 KB

Open AccessArticle

Coupled Impacts of Bed Erosion and Roughness Variation on Stage-Discharge Relationships: A 1D Hydrodynamic Modeling Analysis of the Regulated Jingjiang Reach

by Yanqing Li, Minglong Dai, Dongdong Zhang and Yingqi Chen

Hydrology 2025, 12(12), 311; https://doi.org/10.3390/hydrology12120311 - 22 Nov 2025

Abstract

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model [...] Read more.

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model was validated with high accuracy (Nash-Sutcliffe efficiency >0.94 at key stations) using long-term hydrological data (1996–2022). Four scenarios were simulated: pre-dam conditions, post-dam topography with pre-dam roughness, pre-dam topography with increased roughness, and coupled post-dam changes. A novel scenario-based decomposition framework was developed to isolate individual and coupled factor contributions, advancing beyond traditional descriptive approaches. The results indicate that upstream water level changes are mainly controlled by riverbed erosion (e.g., at the Zhicheng Station: the topographic contribution rate exceeds 80% at a flow rate of 5000 m³/s, resulting in a water level drop of approximately 1.7 m), while downstream, an increase in roughness becomes the dominant factor (e.g., at the Jianli Station: causing a water level rise of about 1.0 m at a flow rate of 13,000 m³/s, with such changes being particularly pronounced under low-flow conditions). Spatially, topographic influence attenuates downstream, whereas roughness sensitivity amplifies in high-sinuosity reaches (bend coefficient: 3.0). Seasonally, the topographic contribution rate remains stable overall during the low-flow period, e.g., within a narrow range of 0.88–0.98 at Zhicheng Station, while roughness effects exhibit negative values in dry periods (November) due to fine sediment deposition. The coupling effect in mid-discharge ranges (15,000–20,000 m³/s) at Jianli partially offsets stage reductions. These findings not only provide critical insights for flood forecasting and navigation management in the Jingjiang Reach but also offer a transferable methodology for quantifying hydro-morphodynamic interactions in global regulated rivers, highlighting the model’s utility in predictive water resource management. Full article

► Show Figures

Figure 1

25 pages, 6426 KB

Open AccessArticle

Dissolved Ion Distribution in a Watershed: A Study Utilizing Ion Chromatography and Non-Parametric Analysis

by Selline Okechi, Keisuke Nakayama and Katsuaki Komai

Hydrology 2025, 12(12), 310; https://doi.org/10.3390/hydrology12120310 - 22 Nov 2025

Abstract

This study presents a unique approach for characterizing ion distribution within the Kushiro River catchment basin, which is characterized by exceptionally high dissolved ion concentrations. principal component analysis, Mann–Whitney U test, and neural network modeling were employed to analyze data from 11 distinct [...] Read more.

This study presents a unique approach for characterizing ion distribution within the Kushiro River catchment basin, which is characterized by exceptionally high dissolved ion concentrations. principal component analysis, Mann–Whitney U test, and neural network modeling were employed to analyze data from 11 distinct locations in two different seasons. The 11 sampling locations were subsequently classified into five distinct groups to facilitate precise analysis of the ion distribution using neural networks. Two principal components were also employed to visualize and interpret our dataset. Compositional similarities and seasonal variations in ion distribution were identified, as well as the key variability patterns, thereby revealing underlying correlations among the dissolved ions. Our findings highlighted that Group 1, encompassing a caldera lake, exhibits the highest dissolved ion concentrations. This observation may be attributed to the geological characteristics of the underlying rock formation. Furthermore, a significant correlation was observed between the major dissolved ions present in the catchment basin, as evidenced by positive correlation coefficients. Conversely, nitrate ions exhibited a negative correlation with F⁻, Cl⁻, and Na⁺ ions. This comprehensive analytical framework offers a robust and insightful tool for determining ion distribution within catchment basins with significant implications for environmental monitoring and sustainable resource management. Full article

(This article belongs to the Section Soil and Hydrology)

► Show Figures

Figure 1

26 pages, 6770 KB

Open AccessFeature PaperArticle

TopEros: An Integrated Hydrology and Multi-Process Erosion Model—A Comparison with MUSLE

by Emmanuel Okiria, Noda Keigo, Shin-ichi Nishimura and Yukimitsu Kobayashi

Hydrology 2025, 12(11), 309; https://doi.org/10.3390/hydrology12110309 - 20 Nov 2025

Abstract

Hydro-erosion is a primary driver of soil degradation worldwide, yet accurate catchment-scale prediction remains challenging because sheet, gully, and raindrop-impact detachment processes operate simultaneously at sub-grid scales. We introduce TopEros, a hydro-erosion model that integrates the hydrological framework of TOPMODEL with three distinct [...] Read more.

Hydro-erosion is a primary driver of soil degradation worldwide, yet accurate catchment-scale prediction remains challenging because sheet, gully, and raindrop-impact detachment processes operate simultaneously at sub-grid scales. We introduce TopEros, a hydro-erosion model that integrates the hydrological framework of TOPMODEL with three distinct erosion modules: sheet erosion, gully erosion, and raindrop-impact detachment. TopEros employs a sub-grid zoning strategy in which each grid cell is partitioned into diffuse-flow (sheet erosion) and concentrated-flow (gully erosion) domains using threshold values of two topographic indices: the topographic index (TI) and the contributing area–slope index (a_itanβ). Applied to the Namatala River catchment in eastern Uganda and calibrated with TI = 15 and a_itanβ = 35, TopEros identified sheet-dominated and gully-prone areas. The simulated specific sediment yields ranged from 95 to 155 Mgha⁻¹yr⁻¹—classified as “high” to “very high”—with gully zones contributing disproportionately large erosion volumes. These results demonstrate the importance of capturing intra-cell heterogeneity: conventional catchment-average approaches can obscure critical erosion hotspots. By explicitly representing multiple soil detachment and transport mechanisms within a unified process-based framework, TopEros has the potential to enhance the realism of catchment-scale erosion estimates and support the precise targeting of soil and water conservation measures. Full article

► Show Figures

Figure 1

28 pages, 7124 KB

Open AccessArticle

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

by Chrysoula Katsora, Evangelos Leivadiotis, Nektaria Papadopoulou, Isavela Monioudi, Efthymia Kostopoulou, Petros Gaganis, Aris Psilovikos and Ourania Tzoraki

Hydrology 2025, 12(11), 308; https://doi.org/10.3390/hydrology12110308 - 18 Nov 2025

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Section Hydrology–Climate Interactions)

► Show Figures

Figure 1

21 pages, 4290 KB

Open AccessArticle

Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training

by Lin Lin, Zhenli Zeng, Chaoqing Tang, Yilin Xie and Qiuhua Liang

Hydrology 2025, 12(11), 307; https://doi.org/10.3390/hydrology12110307 - 17 Nov 2025

Abstract

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, [...] Read more.

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. Full article

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

► Show Figures

Figure 1

Journal Description

Hydrology

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI