Hydrology

17 pages, 2522 KB

Open AccessArticle

Analysis of Soil Infiltration Characteristics and Their Influencing Factors Under Different Vegetation Based on a PLS-SEM Model

by Xuemin Tang, Yutong Peng, Jianli Zhang, Dandan Li, Yang Cao, Weiquan Zhao and Yunjie Wu

Hydrology 2026, 13(7), 197; https://doi.org/10.3390/hydrology13070197 - 22 Jul 2026

Viewed by 196

Abstract

Urban rocky desertification areas are characterized by shallow soils, rock–soil mosaics, and strong human disturbance, so infiltration processes may differ from those in homogeneous soils. However, interactions among multiple controlling factors remain insufficiently quantified. This study compared soil infiltration under artificially restored vegetation [...] Read more.

Urban rocky desertification areas are characterized by shallow soils, rock–soil mosaics, and strong human disturbance, so infiltration processes may differ from those in homogeneous soils. However, interactions among multiple controlling factors remain insufficiently quantified. This study compared soil infiltration under artificially restored vegetation (planted grassland (PG) and planted woodland (PW)), and natural secondary vegetation (secondary grassland (SG) and secondary woodland (SW)). Saturated hydraulic conductivity (Ks) and falling-head duration (T) were measured using falling-head tests on undisturbed soil columns. Soil physical properties were then integrated with partial least squares structural equation modeling (PLS-SEM) to assess the effects of rocky desertification, soil aggregates, and porosity. Soil bulk density was significantly lower under artificially restored vegetation, whereas capillary porosity, non-capillary porosity, and water-holding capacity were significantly higher (p < 0.05). Infiltration performance followed PW > PG > SW > SG. PLS-SEM indicated that rocky desertification (−0.78), porosity (0.51), and aggregates (−0.03) jointly regulated infiltration, with non-capillary porosity as the dominant positive factor. Higher infiltration in artificially restored plots was mainly associated with improved pore structure. These findings support vegetation configuration and soil–water management in urban rocky desertification areas. Full article

(This article belongs to the Section Soil and Hydrology)

► Show Figures

Graphical abstract

18 pages, 28063 KB

Open AccessArticle

Diagnostics of the Average Long-Term Water Discharge of Freely Meandering Rivers Based on Morphological Analysis of Their Channel Configurations

by Alexey Terekhov, Ravil Mukhamediev, Gulshat Sagatdinova and Igor Savin

Hydrology 2026, 13(7), 196; https://doi.org/10.3390/hydrology13070196 - 22 Jul 2026

Viewed by 268

Abstract

Freely meandering rivers flow through gently sloping plains composed of loess and fluvial sediments. Low-gradient alluvial plains are formed without the influence of landscape features such as rock outcrops or other features that distort the flow path. The channel configurations of such rivers, [...] Read more.

Freely meandering rivers flow through gently sloping plains composed of loess and fluvial sediments. Low-gradient alluvial plains are formed without the influence of landscape features such as rock outcrops or other features that distort the flow path. The channel configurations of such rivers, and in particular the size of meanders and oxbow lakes, depend on the average long-term water discharge. Large rivers form large meanders, while small rivers form correspondingly small ones. Morphological analysis of river channel configurations can offer a metric for estimating the average long-term water discharge of a river based solely on the sinuosity of its channel. The study examined six freely meandering rivers in Kazakhstan, with discharges ranging from 4.5 to 760 m³/s and channel slopes from 0.005 to 0.06%. The morphological analysis of river channels was based on the Relative Elevation Model, specifically its version based on the Copernicus Global Digital Elevation Model, with a spatial resolution of 30 m. River channel configurations were approximated using a set of inscribed circles, the diameters of which formed the basis for the river’s average long-term water discharge metric. The largest diameter circles, which could support the river channel with a sector of at least 135°, were expertly inscribed into river bends. The diameters of the inscribed circles within these sets varied from four times for small rivers to ten times for large rivers. These sets of circles, sorted by size, can characterize the average long-term water discharge of the analyzed rivers. For example, a sample of average median values of inscribed circle diameters has a high correlation with the average long-term water discharge, with a linear approximation reliability of R² = 0.997. The scope of the developed method for assessing the average long-term water discharge of freely meandering rivers includes retrospective analysis of changes in average long-term average long-term water discharge. This can provide significant historical depth of analysis, spanning centuries and millennia, since the analysis is based on describing the results of very slow processes of natural deformation of river channels. Thus, the method proposed in this study for assessing the average long-term water discharge of freely meandering rivers based on morphological analysis of their channel configurations expands the arsenal of tools for reconstructing certain paleoclimate elements related to the hydrology of territories. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

► Show Figures

Figure 1

23 pages, 4190 KB

Open AccessArticle

Prioritizing Small-Scale Water Retention Measures Through Spatial Differentiation of Dominant Runoff Processes

by Katharina Pilar von Pilchau, Christoph Mudersbach, Udo Nehren and Klaus Maas

Hydrology 2026, 13(7), 195; https://doi.org/10.3390/hydrology13070195 - 22 Jul 2026

Viewed by 330

Abstract

In order to mitigate the negative effects of heavy rainfall events, natural water retention measures (NWRM)—such as hedges, erosion control strips, vegetated drainage channels, wooded strips, retention basins and ditch pockets—have gained renewed attention as an effective climate adaptation strategy. To identify potential [...] Read more.

In order to mitigate the negative effects of heavy rainfall events, natural water retention measures (NWRM)—such as hedges, erosion control strips, vegetated drainage channels, wooded strips, retention basins and ditch pockets—have gained renewed attention as an effective climate adaptation strategy. To identify potential areas for NWRM, this study applied and methodologically expanded an existing approach for identifying dominant runoff processes (DRPs) to an agricultural sub-catchment in the Weserbergland region of Germany. The DRP were determined using a Geographic Information System (GIS) and validated through field surveys. Potential areas for water retention within the same runoff process classes were identified for three defined objectives: improving infiltration, extending flow paths, and redirecting runoff to surrounding areas. Spatial differentiation was achieved using accumulated catchment area and overland flow distance. The watershed is predominantly characterized by surface runoff (Hortonian Overland Flow). Field validation confirmed the DRP classification for around two-thirds of the study area, with deviations occurring predominantly on arable land. Supplementing the DRP approach with a topographic analysis allowed for further differentiation, focusing on small, topographically defined sub-watersheds. The identified areas offer significant potential for interventions. Combined with supplementary data, analyses of the water network and the involvement of local stakeholders, the resulting potential map provides a solid basis for planning smaller-scale water retention measures. Full article

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

► Show Figures

Figure 1

15 pages, 5184 KB

Open AccessArticle

Mechanical Load-Induced PFAS Transport in the Vadose Zone

by Zhi-He Jin

Hydrology 2026, 13(7), 194; https://doi.org/10.3390/hydrology13070194 - 22 Jul 2026

Viewed by 263

Abstract

PFAS-laden fluid-filled porous media may be subjected to various mechanical loads which induce solid deformation and fluid flow and hence PFAS transport. This work employs a poroelasticity theory for unsaturated porous media to address the coupled solid deformation, fluid flow and PFAS transport [...] Read more.

PFAS-laden fluid-filled porous media may be subjected to various mechanical loads which induce solid deformation and fluid flow and hence PFAS transport. This work employs a poroelasticity theory for unsaturated porous media to address the coupled solid deformation, fluid flow and PFAS transport in the vadose zone subjected to a mechanical load. The governing equation of the aqueous PFAS concentration is derived based on the PFAS mass balance that also considers the water content variation in the pores due to the solid deformation. Vertical PFAS transport in a finite soil layer under mechanical compression is studied using a finite difference method and the solutions of the pore fluid pressures and volumetric strain. Numerical results of the aqueous concentrations of perfluorooctane sulfonic (PFOS) in loamy sand and clay loam indicate that mechanical compression has pronounced effects on the spatial distribution of PFOS. In a loamy sand with relatively higher permeability, mechanical compression at the top drained surface leads to movement of PFOS from the topsoil to the surface thereby reducing the PFOS concentration in the topsoil especially at higher water saturations. The PFOS concentration in the subsoil, however, is not significantly influenced. The effect of mechanical compression on the PFOS concentration distribution in a clay loam can also be observed but is not as significant as in the loamy sand. The mechanical loading effects may be further explored to develop new technologies for PFAS risk assessment and remediation strategies. Full article

(This article belongs to the Topic Transport, Transformation and Cycling of Elements in Water and Soil and Their Response to Human Activities)

► Show Figures

Figure 1

25 pages, 5187 KB

Open AccessArticle

A Sensitivity Study of Vertical and Horizontal River–Aquifer Exchange and Implications for Flood Attenuation

by Gadadhara de Figueiredo Ferraz and Tamás Krámer

Hydrology 2026, 13(7), 193; https://doi.org/10.3390/hydrology13070193 - 18 Jul 2026

Viewed by 484

Abstract

River–aquifer exchange during floods controls groundwater recharge and bank storage, yet its sensitivity to floodplain geometry, flood duration, and subsurface permeability remains insufficiently quantified. A coupled model integrating one-dimensional surface-water flow, two-dimensional groundwater flow, and vertically resolved unsaturated floodplain infiltration was parameterized using [...] Read more.

River–aquifer exchange during floods controls groundwater recharge and bank storage, yet its sensitivity to floodplain geometry, flood duration, and subsurface permeability remains insufficiently quantified. A coupled model integrating one-dimensional surface-water flow, two-dimensional groundwater flow, and vertically resolved unsaturated floodplain infiltration was parameterized using observation-derived bank-storage estimates from Hungarian Danube floods. Idealized scenarios evaluated the effects of floodplain width, flood duration, and aquifer and soil permeability on river–aquifer exchange, bank storage, and flood attenuation. Simulations indicated that, under the investigated conditions, vertical floodplain infiltration dominates exchange processes, accounting for approximately 82–92% of total exchange and bank storage, whereas horizontal riverbed exchange remains secondary. Floodplain geometry and flood duration exerted influences on bank storage and flood attenuation comparable to those of subsurface permeability. Wider floodplains enhanced infiltration, storage capacity, and flood attenuation. Short floods produced high but transient infiltration and strong peak attenuation, whereas longer floods promoted sustained infiltration, larger bank storage, and delayed attenuation. Subsurface permeability regulated exchange efficiency but exhibited a nonlinear influence on bank storage. These results demonstrated that flood attenuation during overbank flooding depends not only on river–aquifer exchange magnitude, but also on its partitioning and the temporal evolution of subsurface storage. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

► Show Figures

Figure 1

27 pages, 7560 KB

Open AccessArticle

The Effects of Input Scale and Metric on Groundwater Level Forecasting with Deep Learning

by Halima Hilal, Nourelhouda Karmouda, Tarik Bouramtane, Youssef Hamou-Ali, Ismail Mohsine, Houssne Bouimouass, Hassan Mosaid, Mounia Tahiri, Nadia Kassou, Ilias Kacimi and Marc Leblanc

Hydrology 2026, 13(7), 192; https://doi.org/10.3390/hydrology13070192 - 17 Jul 2026

Viewed by 474

Abstract

The Normalized Difference Vegetation Index (NDVI) is widely used as an indicator of irrigation activity in arid and semi-arid agricultural regions. This study evaluates how NDVI extraction scale and statistical metric influence groundwater level prediction accuracy in the irrigated Tadla Plain, MoroccoA total [...] Read more.

The Normalized Difference Vegetation Index (NDVI) is widely used as an indicator of irrigation activity in arid and semi-arid agricultural regions. This study evaluates how NDVI extraction scale and statistical metric influence groundwater level prediction accuracy in the irrigated Tadla Plain, MoroccoA total of 96 Long Short-Term Memory (LSTM) models were developed by combining six NDVI extraction scales, from the well pixel to 20,000 m buffers, and four statistical metrics: mean, median, maximum, and minimum. Results demonstrate that extraction scale is a critical factor controlling model performance. Across the four monitored wells, larger buffers (≥2500 m) generally outperformed smaller ones (≤1000 m), with the optimal scale occurring at 15,000 m for three wells, while one well achieved its best performance at the 1000 m scale. The best models achieved RMSE values between 0.09 and 0.625 m and R² values ranging from 0.94 to 0.997. Maximum NDVI provided the highest predictive accuracy for three wells, whereas minimum NDVI performed best for one well. Statistical analyses further confirmed that extraction scale generally exerts a stronger influence on prediction performance than the choice of NDVI metric. Spatial validation revealed that irrigated areas more than doubled between 2001 and 2017, and model performance improved as NDVI captured this broader irrigation footprint. These findings suggest that groundwater level variations in the Tadla Plain are more strongly associated with NDVI signals extracted at broader spatial scales than with strictly local vegetation conditions around individual wells, highlighting the importance of optimizing both NDVI extraction scale and metric for groundwater forecasting in irrigated semi-arid regions. Full article

► Show Figures

Graphical abstract

30 pages, 11364 KB

Open AccessArticle

A Data-Driven Approach to Close the Water Balance of a Cascaded Multiple Reservoir–Lake System

by Máté Chappon, Katalin Bene and Richard Ray

Hydrology 2026, 13(7), 191; https://doi.org/10.3390/hydrology13070191 - 16 Jul 2026

Viewed by 526

Abstract

This study presents a data-driven methodology to develop and close the continuous water balance of a cascaded hydrological system consisting of two upstream regulating reservoirs and a downstream lake. The analysis is based on monthly hydrometeorological and hydrographic data for the period 1998–2024. [...] Read more.

This study presents a data-driven methodology to develop and close the continuous water balance of a cascaded hydrological system consisting of two upstream regulating reservoirs and a downstream lake. The analysis is based on monthly hydrometeorological and hydrographic data for the period 1998–2024. A baseline water balance model using raw measured data was first implemented, followed by two bias correction approaches based on multiple linear regression coefficient estimation. The first approach applies uniform coefficients across all months, while the second accounts for seasonal variability by estimating coefficients only for the December–May period, when systematic errors are most pronounced. Model performance was evaluated using calibration (1998–2017) and validation (2018–2024) periods, with Nash–Sutcliffe efficiency (NSE) and residual diagnostics used as evaluation metrics. The baseline model showed substantial cumulative deviations due to systematic errors (NSE: −0.28 and −5.13 for the two reservoirs and −18.70 for the lake), confirming the need for bias correction. Both correction approaches significantly improved model performance, with NSE values up to 0.89 for reservoirs and 0.83–0.86 for the lake during calibration. In validation, the seasonally adjusted model performed more stably in simulating water levels for Lake Velence (NSE = 0.72) than the uniform coefficient model (NSE = 0.60), particularly under extreme hydrological conditions. Residual analysis further indicated improved independence and homoscedasticity when seasonal structure was considered. The results demonstrate that water balance closure in the system is affected by distributed errors across multiple components rather than a single dominant source. The proposed methodology provides a practical, scalable framework for reconstructing consistent water balance time series in data-limited, regulated systems and supports the development of water management scenarios. Full article

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

► Show Figures

Figure 1

19 pages, 2935 KB

Open AccessArticle

Bridge Scour Analysis for Rocks: Classification, Method Selection, and Practical Case Studies

by Darud E. Sheefa, Stanley Vitton, Zhen (leo) Liu and Brian Barkdoll

Hydrology 2026, 13(7), 190; https://doi.org/10.3390/hydrology13070190 - 16 Jul 2026

Viewed by 378

Abstract

Despite the availability of several scour calculation methods, bridge scour analysis for scour susceptible rocks is much less understood than its counterpart for sands, because scour calculation equations were traditionally obtained using flume tests with sands. As a result, there are three major [...] Read more.

Despite the availability of several scour calculation methods, bridge scour analysis for scour susceptible rocks is much less understood than its counterpart for sands, because scour calculation equations were traditionally obtained using flume tests with sands. As a result, there are three major knowledge gaps in the bridge scour analysis for susceptible rocks: (1) a rock classification system is needed for scour considerations to serve as a pre-screening process before conventional scour analysis involving sampling, erodibility testing, and scour calculation, (2) there has been a lack of information to guide the scour analysis for rocks including detailed procedures, needed and available data, and interpretation of results, especially for real bridge sites, and (3) detailed comparisons between the available rock scour calculation methods and the widely used sand-based scour depth calculation equations are rare. This paper presents a study to bridge the above knowledge gaps. First, a rock classification system was proposed for the bedrock of Michigan to exemplify the use of a pre-screening tool for rock preliminary scour susceptibility determination. Then, two methods were selected for two modes of scour that are common to bridges: the Erodibility Index Method for the quarrying and plucking mode of rock scour and the NCHRP-717 method for the abrasion mode. Case studies were conducted at two real bridge sites using these methods as well as sand-based equations. These case studies are intended to demonstrate practical implementation using available project data, compare the outcomes of different scour-analysis methods, and identify common data limitations and implementation challenges. Full article

(This article belongs to the Section Soil and Hydrology)

► Show Figures

Figure 1

20 pages, 9386 KB

Open AccessArticle

Ecological Water Demand and Near-Natural Water-Replenishment Schemes for Wetlands in Semi-Arid Regions

by Mingze Xiao, Fangli Su, Di Wang, Zining Wang, Pengxing Su, Hao Xu, Fei Song, Chao Wei, Haifu Li and Shuang Song

Hydrology 2026, 13(7), 189; https://doi.org/10.3390/hydrology13070189 - 13 Jul 2026

Viewed by 269

Abstract

Semi-arid wetlands are highly sensitive to changes in hydrological regimes, as strong evaporation often exceeds limited natural recharge. Ecological water replenishment is widely used to restore these systems, but schemes designed only to meet water-volume targets may cause excessive hydrodynamic disturbance, promote sediment [...] Read more.

Semi-arid wetlands are highly sensitive to changes in hydrological regimes, as strong evaporation often exceeds limited natural recharge. Ecological water replenishment is widely used to restore these systems, but schemes designed only to meet water-volume targets may cause excessive hydrodynamic disturbance, promote sediment resuspension, and increase the release of internal pollutants. In this study, we developed an ecological water-replenishment assessment framework for Chahannaoer Wetland that incorporates ecological water-demand thresholds, suspended-solids disturbance, and an AHP–entropy weight–TOPSIS decision model. Using hydrological and meteorological data from 2014 to 2024, six replenishment scenarios were evaluated in terms of water-balance recovery, disturbance control, and habitat suitability. The results show that Chahannaoer Wetland experienced a persistent evaporation-dominated water deficit. The mean annual natural recharge was 0.225 × 10⁸ m³, with a mean annual ecological water shortage of 1.03 × 10⁸ m³ and an evapotranspiration-to-recharge ratio of 3.42–4.56. Based on the previous comprehensive water-quality assessment using DO, COD, NH₃-N, TN, and TP, the minimum water volume required to maintain Class IV water quality was 0.86 × 10⁸ m³, whereas the suitable ecological water demand ranged from 1.27 × 10⁸ to 1.56 × 10⁸ m³. With the total replenishment volume held constant, centralized replenishment met the required water volume but substantially increased near-bed disturbance and sediment resuspension risk. By contrast, decentralized uniform replenishment performed best, with the highest relative closeness coefficient of 0.9105, a disturbance index of approximately 0.32, and water depths maintained within the suitable habitat range of 30–50 cm. These findings suggest that ecological restoration in semi-arid wetlands should move beyond volume-based water supplementation and pay greater attention to the timing, pathway, and hydrodynamic effects of replenishment. The proposed framework provides a quantitative basis for optimizing ecological water replenishment in evaporation-dominated wetlands and other inland lakes in arid and semi-arid regions. Full article

(This article belongs to the Topic Linking Agricultural–Hydrological Processes and Extreme Events Under a Changing Climate)

► Show Figures

Figure 1

22 pages, 8899 KB

Open AccessArticle

Topographic and Climatic Controls on Depth–Duration–Frequency Curve Parameters in the Gargano Promontory (Southern Italy)

by Gabriele Iemmolo, Andrea Petroselli, Nunzio Angiola and Ciro Apollonio

Hydrology 2026, 13(7), 188; https://doi.org/10.3390/hydrology13070188 - 12 Jul 2026

Viewed by 533

Abstract

Accurate estimation of depth–duration–frequency (DDF) curves is essential for hydrological analyses and flood-risk mitigation. Regionalization methods are particularly important in areas with limited observations, but their performance may deteriorate where topography and climate generate strong spatial variability. This study investigates the Gargano promontory [...] Read more.

Accurate estimation of depth–duration–frequency (DDF) curves is essential for hydrological analyses and flood-risk mitigation. Regionalization methods are particularly important in areas with limited observations, but their performance may deteriorate where topography and climate generate strong spatial variability. This study investigates the Gargano promontory in southern Italy, an area that is commonly treated as a single hydrologically homogeneous zone despite its marked morphological and climatic contrasts. Annual maximum rainfall data from eight rain gauges, together with topographic and climatic descriptors, were analyzed to assess whether local factors help explain the variability of DDF-curve parameters. The analysis focused on elevation, distance from the sea, and a wind-effect index derived from a digital elevation model. The results indicate that the regional behavior of extreme rainfall is not spatially uniform and that several DDF-curve parameters are influenced by local physiographic controls. Different controls emerge for different station groups, and a physiographically based subdivision of the Gargano promontory into windward and leeward sectors provides a more coherent representation of DDF-curve parameters than the currently adopted single-zone regionalization. Full article

► Show Figures

Figure 1

28 pages, 46903 KB

Open AccessArticle

Projected Global Changes in Severe and Extreme Drought Occurrence: A CMIP6 Multi-Model Assessment Using SPI, SPEI, and Concurrent SPI-SPEI Conditions

by Aili Yang, Jiaona Guo, Yueyu Su, Yurui Fan and Xiuquan Wang

Hydrology 2026, 13(7), 187; https://doi.org/10.3390/hydrology13070187 - 12 Jul 2026

Viewed by 280

Abstract

Understanding how different drought indicators characterise future drought conditions is essential for climate monitoring and adaptation planning. This study quantified annual severe- and extreme-drought occurrence rates at the 1- and 3-month timescales using the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index [...] Read more.

Understanding how different drought indicators characterise future drought conditions is essential for climate monitoring and adaptation planning. This study quantified annual severe- and extreme-drought occurrence rates at the 1- and 3-month timescales using the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and their concurrent signals. Historical conditions during 1951–2010 were compared with projections for 2041–2100 under SSP245 and SSP585 using an ensemble of 12 CMIP6 General Circulation Models. Inter-model uncertainty was assessed using the 5th and 95th ensemble quantiles. The results reveal marked contrasts between precipitation-based and potential-evapotranspiration-sensitive drought indicators. Globally, SPI-based severe-drought occurrence decreases under both scenarios, whereas SPI-based extreme-drought occurrence increases, particularly at the 3-month timescale under SSP585. Stronger and more spatially extensive increases are identified using SPEI. The global mean occurrence rate of SPEI1-based extreme drought increases from 0.231 month/year historically to 0.791 month/year under SSP245 and 1.197 month/year under SSP585. For SPEI3, the corresponding rate increases from 0.207 to 1.090 and 1.716 month/year, respectively, with approximately 92.3% of land grid cells showing increases under SSP585. Concurrent SPI-SPEI severe-drought occurrence decreases, while concurrent extreme-drought occurrence increases across approximately 70–77% of land grid cells. This contrast indicates a redistribution of concurrent drought months from the severe to the extreme severity class under a mutually exclusive classification scheme, particularly at the 3-month timescale. The Mediterranean region, the Amazon and other parts of South America, southern Africa, parts of West and Central Asia, and Australia consistently emerge as major hotspots. Ensemble-quantile results support the direction of increasing SPEI-based and concurrent extreme-drought occurrence, although substantial uncertainty remains in the magnitude of change. These findings demonstrate the value of jointly considering precipitation-based and evaporative-demand-sensitive indicators in drought monitoring and regional climate adaptation planning. Full article

► Show Figures

Figure 1

24 pages, 56458 KB

Open AccessArticle

Surface Runoff Risk and Resilience Planning in a Plateau City Under System Non-Stationarity

by Xinyu Wang, Ningkun Kang, Zihan Zhu, Jingli Zhang, Guanyu Chen, Samuel A. Cushman, Guifang Wang, Yawen Wu and Tian Bai

Hydrology 2026, 13(7), 186; https://doi.org/10.3390/hydrology13070186 - 11 Jul 2026

Viewed by 219

Abstract

Variations in urban surface runoff are often attributed to static infrastructure, neglecting the non-stationary hydrological responses induced by rapid urbanization and intricate micro-topography. This study introduces a Pressure–Trend–Pulse (PTP) framework to examine surface runoff dynamics in Kunming, China. By integrating continuous Soil and [...] Read more.

Variations in urban surface runoff are often attributed to static infrastructure, neglecting the non-stationary hydrological responses induced by rapid urbanization and intricate micro-topography. This study introduces a Pressure–Trend–Pulse (PTP) framework to examine surface runoff dynamics in Kunming, China. By integrating continuous Soil and Water Assessment Tool (SWAT) simulations (2005–2024), Sen’s slope estimation, the Mann–Kendall test, robust residual analysis, and Self-Organizing Map (SOM) clustering, we quantify these multi-dimensional changes. The findings indicate: (1) runoff displays a structural north–south gradient, with the generation centroid migrating northward at a rate of 2.3 km per decade; (2) non-stationary positive trends (0.16 mm/year) are exclusively concentrated in northern sub-basins, which constitute 26.74% of the total area, thereby exacerbating long-term cumulative pressure; and (3) detrended residual analysis reveals high-frequency pulse volatility predominantly in the southern sink areas. Overlaying 139 historical waterlogging points confirms that trend-driven and pulse-driven risks account for 30.22% and 13.67% of urban disasters, respectively. Furthermore, approximately 22% of waterlogging occurrences fall within non-significant downstream zones, implying a potential upstream-downstream source–sink decoupling. The PTP framework highlights the necessity of differentiated resilience planning: upstream source-control and downstream adaptive buffering. Full article

► Show Figures

Figure 1

32 pages, 6775 KB

Open AccessArticle

Flood Susceptibility Mapping: Scenario-Based Multi-Criteria Decision-Making Versus Random Forest Model

by Mehdi Rahimi, Bahram Malekmohammadi, Mohammad Karimi Firozjaei, Reza Kerachian and Farhad Bahmanpouri

Hydrology 2026, 13(7), 185; https://doi.org/10.3390/hydrology13070185 - 11 Jul 2026

Viewed by 518

Abstract

Floods are among the most destructive natural hazards, posing a significant risk to lives and infrastructure worldwide. Effective flood risk management demands precise, multidimensional approaches. In this direction, the current study aims to evaluate and compare two methods for flood-risk assessment: the scenario-based [...] Read more.

Floods are among the most destructive natural hazards, posing a significant risk to lives and infrastructure worldwide. Effective flood risk management demands precise, multidimensional approaches. In this direction, the current study aims to evaluate and compare two methods for flood-risk assessment: the scenario-based Ordered Weighted Averaging (OWA) and the data-driven Random Forest (RF) method. To this end, the Great Karun watershed in Iran was chosen due to its complex hydrological and climatic conditions. Hydro-climatic, hydrological, topographic, land-cover datasets, and actual flood observations were applied and analyzed based on fifteen influencing factors recommended by expert opinion. In the OWA approach, while factor weights were determined using the Best-Worst Method, flood-risk maps were produced based on five scenarios: very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. In the RF approach, factor importance index was calculated via the mean decrease impurity algorithm, and the model was trained to generate flood-risk maps. Results showed that distance from rivers and slope were the most influential factors in OWA, while precipitation and flow accumulation dominated in RF. Prediction rate for OWA scenarios ranged from 1.4 to 3.5%, while RF achieved 16.0%, and the Area Under the Curve (AUC) was 0.984. Optimistic scenarios overestimated, and pessimistic scenarios underestimated risk, with the OWA intermediate scenario most closely matching RF results. RF demonstrated superior performance for flood-risk classification, highlighting its applicability for precise flood management. Overall, this study presents a novel comparative framework by integrating scenario-based OWA-BWM and Random Forest approaches to investigate the effects of decision-maker preferences and data-driven learning on flood susceptibility mapping. Full article

(This article belongs to the Special Issue Advancements in Measuring and Modelling River Flow Characteristics and Sediment Transport)

► Show Figures

Figure 1

24 pages, 16916 KB

Open AccessArticle

Deciphering Urban Flood Drivers: An Explainable Machine Learning Approach to Vulnerability Assessment in Indonesian Catchments

by Ahyahudin Sodri, Geovanny Branchiny Imasuly, Nuraeni Nuraeni and Annisa Layyina Ihsani

Hydrology 2026, 13(7), 184; https://doi.org/10.3390/hydrology13070184 - 11 Jul 2026

Viewed by 298

Abstract

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme [...] Read more.

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R² = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia. Full article

(This article belongs to the Special Issue Urban Hydrology and Hydroclimate Resilience for Climate Change Adaptation)

► Show Figures

Figure 1

16 pages, 3423 KB

Open AccessArticle

From Late Nineteenth-Century Drought to Modern Pluvial Conditions: Tree-Ring Reconstructions of Precipitation and Streamflow in the Central Alps

by Julianne Webb, Maggie Duncan, Glenn Tootle, Wolfgang Gurgiser and Abel Andrés Ramírez Molina

Hydrology 2026, 13(7), 183; https://doi.org/10.3390/hydrology13070183 - 9 Jul 2026

Viewed by 317

Abstract

Understanding long-term hydroclimatic variability in the central Alps is essential when placing recent changes in precipitation and streamflow within a broader temporal context. This study reconstructs warm-season hydroclimatic variability in the central Alps using tree-ring-based hydroclimatic proxies from the Old World Drought Atlas [...] Read more.

Understanding long-term hydroclimatic variability in the central Alps is essential when placing recent changes in precipitation and streamflow within a broader temporal context. This study reconstructs warm-season hydroclimatic variability in the central Alps using tree-ring-based hydroclimatic proxies from the Old World Drought Atlas (OWDA). Seasonal April–May–June–July–August (AMJJA) precipitation at Innsbruck, Austria, and seasonal May–June–July–August (MJJA) streamflow at the St. Jodok gauge were reconstructed using OWDA self-calibrating Palmer Drought Severity Index (scPDSI) predictors and moving-window Stepwise Linear Regression (SLR) models. Calibration windows of 30, 40, and 50 years were developed to account for temporal variability in predictor–climate relationships, and reconstruction uncertainty was quantified using multi-model ensemble bounds. An independent Deep Learning reconstruction was also developed for precipitation to provide an assessment of reconstruction skill and long-term climate trends. Specifically, the results demonstrate a robust reconstruction skill, with mean calibration R² values of 0.65 for streamflow and 0.59 for precipitation. The streamflow reconstruction indicates that recent sustained increases represent the strongest positive anomaly in approximately 650 years, while reconstructed precipitation suggests recent decades are among the wettest sustained intervals of the last ~2000 years. Both records reveal a pronounced transition from severe late 19th-century drought conditions to persistent modern pluvial conditions. Agreement between regression and Deep Learning reconstructions supports the robustness of the identified long-term wetting trend and highlights the exceptional nature of recent hydroclimatic conditions in the central Alps. Full article

► Show Figures

Figure 1

32 pages, 5863 KB

Open AccessArticle

A Probabilistic Dynamic Reservoir Operation Framework (PDROF) for Adaptive Reservoir Operation Under Climate Variability: A Case Study of Kwan Phayao, Thailand

by Anujit Phumiphan and Anongrit Kangrang

Hydrology 2026, 13(7), 182; https://doi.org/10.3390/hydrology13070182 - 8 Jul 2026

Viewed by 418

Abstract

Reservoir operation under hydrological uncertainty has become increasingly challenging under changing climate conditions. This study proposes a Probabilistic Dynamic Reservoir Operation Framework (PDROF) that integrates stochastic inflow modeling, Monte Carlo simulation, and dynamic rule extraction for adaptive reservoir management. Historical inflow records were [...] Read more.

Reservoir operation under hydrological uncertainty has become increasingly challenging under changing climate conditions. This study proposes a Probabilistic Dynamic Reservoir Operation Framework (PDROF) that integrates stochastic inflow modeling, Monte Carlo simulation, and dynamic rule extraction for adaptive reservoir management. Historical inflow records were transformed into stochastic inflow ensembles and propagated through reservoir operation simulations to generate reservoir storage trajectories under varying hydrological conditions. From these trajectories, a representative operational rule, referred to as the Most Likely Line (MLL), was extracted to characterize the dominant storage behavior of the system. The results demonstrate that conventional deterministic rule curves are constrained by predefined hydrological classifications and limited flexibility under variable inflow conditions. In contrast, the proposed framework effectively captures seasonal variability and propagates hydrological uncertainty throughout the operational cycle. Long-term simulation over a 23-year period resulted in a total spill volume of 17.74 million cubic meters (MCM), with spill events occurring in only 7 months, indicating improved operational robustness and storage stability. A real flood event in 2024 further demonstrated reductions of 47.42 MCM in spill volume and 3.46 MCM in reservoir storage compared with conventional operation. These improvements are attributed to the anticipatory storage behavior of the MLL-based operational rule, which preserves flood-buffer capacity prior to peak inflow periods and reduces the likelihood of uncontrolled spill events. The proposed framework provides a practical transition from deterministic reservoir operation toward uncertainty-aware and adaptive water resources management. The methodology is scalable to data-scarce and climate-sensitive regions and can be further extended through real-time forecasting and multi-objective optimization in future studies. Full article

(This article belongs to the Special Issue Sustainable Urban Water Resources Management)

► Show Figures

Figure 1

16 pages, 4712 KB

Open AccessArticle

Numerical Modeling of Nonlinear Groundwater Flow in a Heterogeneous Four-Layer Porous Medium

by Normakhmad Ravshanov, Kamola Shadmanova and Istam Shadmanov

Hydrology 2026, 13(7), 181; https://doi.org/10.3390/hydrology13070181 - 7 Jul 2026

Viewed by 322

Abstract

This paper presents a comprehensive numerical modeling of nonlinear groundwater flow in a synthetic heterogeneous four-layer porous medium. Multilayered aquifer systems present significant modeling challenges due to nonlinear filtration and interlayer exchange processes. The mathematical model consists of four coupled nonlinear parabolic partial [...] Read more.

This paper presents a comprehensive numerical modeling of nonlinear groundwater flow in a synthetic heterogeneous four-layer porous medium. Multilayered aquifer systems present significant modeling challenges due to nonlinear filtration and interlayer exchange processes. The mathematical model consists of four coupled nonlinear parabolic partial differential equations, where the nonlinearity arises from the dependence of hydraulic conductivity on hydraulic head. Vertical exchange between layers is described by Darcy’s law through separating aquicludes. The system is solved using a fully implicit finite-difference scheme by employing an alternating-direction implicit approach, resulting in a block-tridiagonal system of equations. The model is verified using analytical solutions and mass conservation tests. Application to a synthetic aquifer system demonstrates the model’s ability to reproduce complex transient behavior, including delayed response of upper layers to pumping and asymmetry of water-level drawdown cones due to nonlinear conductivity. The model’s greatest sensitivity is observed to the conductivity of the pumped layer and the vertical conductivity of the separating layers. The proposed approach represents a robust tool for groundwater management in structurally complex geological settings. Full article

(This article belongs to the Topic Advances in Groundwater Science and Engineering)

► Show Figures

Figure 1

41 pages, 35693 KB

Open AccessArticle

Index-Based Vulnerability Assessment—A Multi-Dimensional Index as a Tool for Capturing the Effects of Nature-Based Solutions for Flood Mitigation

by Jelena Kovačević-Majkić, Nikola Rosić, Dragoljub Štrbac, Vujica Šarenac and Andrijana Todorović

Hydrology 2026, 13(7), 180; https://doi.org/10.3390/hydrology13070180 - 7 Jul 2026

Viewed by 724

Abstract

This study presents the Multi-dimensional Flood Vulnerability Index (M-FLOVI), calculated by using an index-based method specifically tailored to capture the impact of nature-based solutions (NbSs) on vulnerability. It aggregates five vulnerability dimensions (physical, economic, environmental, social and institutional) into a single index within [...] Read more.

This study presents the Multi-dimensional Flood Vulnerability Index (M-FLOVI), calculated by using an index-based method specifically tailored to capture the impact of nature-based solutions (NbSs) on vulnerability. It aggregates five vulnerability dimensions (physical, economic, environmental, social and institutional) into a single index within a multi-level framework. Each dimension is calculated from a set of indicators that can be computed with moderate data demands. These calculations generally require information about buildings, infrastructure, land cover, population, protected areas and cultural heritage, which can partly be obtained from open-access data. M-FLOVI ranges between 0 and 1, and it can be readily mapped and combined with flood hazard to produce flood risk maps. This paper elaborates a step-by-step M-FLOVI calculation in the Tamnava River Basin, Serbia, where various NbSs were proposed. Under the baseline conditions, most of the study area exhibits either moderate (83.5%) or low vulnerability (15.8%). These NbSs decrease future vulnerability in 6.6 km² (1.34%) of the study area. Afforestation (0.59%) and retention ponds (0.42%) decrease environmental vulnerability, while flood plain restoration (0.33%), which is expected to create a protected bird habitat, increases environmental vulnerability. These results suggest that M-FLOVI can effectively capture NbSs’ impacts on future vulnerability to floods. Full article

(This article belongs to the Special Issue Advances in Nature-Based Solutions for Hydrometeorological Risk Reduction)

► Show Figures

Graphical abstract

22 pages, 63898 KB

Open AccessArticle

Local-Scale Groundwater Modeling of Surface–Groundwater Interaction in a Complex Hydrological Setting

by Juan Pescador, Luis Silva, Boris Lora-Ariza, Juan Felipe Landinez, Mónica Vaca, Pedro Romero, Adriana Piña and Leonardo David Donado

Hydrology 2026, 13(7), 179; https://doi.org/10.3390/hydrology13070179 - 6 Jul 2026

Viewed by 852

Abstract

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica [...] Read more.

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica town, where the Lebrija River, the Musanda floodplain lake, and groundwater system converge. The numerical model incorporates: (i) the three-dimensional distribution of geological units and lithology; (ii) water level observations from the Musanda floodplain lake; (iii) stage records from the Lebrija River; (iv) boundary conditions and flux estimates inherited from a previous regional groundwater model; and (v) hydraulic heads from two monitoring wells and five community wells. Steady-state and transient conditions were calibrated, and a sensitivity analysis was performed to identify the parameters that most strongly control surface water–groundwater exchange. The simulations reproduce seasonal groundwater level trends and demonstrate the exchange pathways among the river, floodplain lake, and groundwater system. Results indicate dual behavior: during wet periods, flooding of the Musanda floodplain lake driven by high river levels seeps into the underlying aquifer, whereas in dry periods the floodplain lake reverses its role and becomes a principal discharge boundary. This local-scale, boundary-driven approach provides a computationally tractable framework to quantify SW–GW exchange in data-scarce tropical floodplains and supports monitoring design and water-supply management. Full article

(This article belongs to the Special Issue Integrated Surface Water and Groundwater Resource Management, 2nd Edition)

► Show Figures

Figure 1

29 pages, 12162 KB

Open AccessArticle

Spatiotemporal Patterns and Nonlinear Drivers of Water Yield in Inner Mongolia

by Cairui Fan, Teng Wang, Xiu Li, Bo Zhai and Dandan Luo

Hydrology 2026, 13(7), 178; https://doi.org/10.3390/hydrology13070178 - 3 Jul 2026

Viewed by 259

Abstract

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze [...] Read more.

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze the spatial pattern and nonlinear driving mechanism of water yield depth for sustainable water resource management. Based on the InVEST model, water yield depth during 2001–2024 was simulated, and trend analysis was conducted. Annual XGBoost models with SHAP were used to explain nonlinear driver effects. Results showed a significant east-high and west-low pattern, with significantly increasing and decreasing areas accounting for 12.35% and 4.5%, respectively. Precipitation was the dominant driver, with higher ∣SHAP∣ values in wet years than in dry years. Zonal SHAP showed Pre led in all zones (48.8%, 63.5%, 37.7%), with secondary drivers shifting from forest/topography in the East to temperature in the West. SHAP values increased rapidly after precipitation exceeded thresholds of 200–300 mm in dry years and 400–500 mm in wet years. Under high precipitation, precipitation–non-forest interactions increased rapidly, whereas forest interactions changed little or became negative, showing a scissor-like divergence pattern. XGBoost reproduced the InVEST-simulated water yield depth well (R² = 0.91 ± 0.03). This workflow provides a reproducible pathway for water resource assessment in arid and semi-arid regions. Full article

(This article belongs to the Topic Linking Agricultural–Hydrological Processes and Extreme Events Under a Changing Climate)

► Show Figures

Figure 1

20 pages, 6052 KB

Open AccessArticle

Distributed Estimation of the Curve Number (CN) in Continental Ecuador Using Machine Learning, Official Geo-Pedological Data, and Field-Based Hydrological Validation

by Carlos Andrés Maldonado Chávez, Benito Guillermo Mendoza Trujillo, Andrés Santiago Cisneros Barahona, Guido Patricio Santillán Lima, Nelson Bravo Yumi, Tamia Samai Nuñez Cruz and María Rafaela Viteri Uzcategui

Hydrology 2026, 13(7), 177; https://doi.org/10.3390/hydrology13070177 - 3 Jul 2026

Viewed by 1437

Abstract

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed [...] Read more.

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

(This article belongs to the Special Issue Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model)

► Show Figures

Figure 1

16 pages, 2625 KB

Open AccessArticle

Water Availability and Precipitation Indicators in the Muriaé River Basin, Southeast Brazil

by Eduardo Cochrane Novo, Monica de Aquino Galeano Massera da Hora and José Paulo Soares de Azevedo

Hydrology 2026, 13(7), 176; https://doi.org/10.3390/hydrology13070176 - 2 Jul 2026

Viewed by 856

Abstract

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and [...] Read more.

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and the Q95 low flow parameter were analyzed to evaluate hydrological variability and drought conditions. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator, and Pearson correlation analysis was applied to assess the relationship between precipitation and low flow availability. The results showed marked temporal variability in precipitation and hydrological conditions throughout the basin. Although statistically significant increasing trends in annual precipitation were identified at Carangola and Patrocínio do Muriaé, no generalized long-term reduction in precipitation was observed in the MRB. In contrast, Q95 exhibited reductions at all monitored stations, with decadal decreases ranging from approximately 31% at Carangola to 56% at Itaperuna. The RAI analysis indicated predominance of very dry and extremely dry events during the most recent decade, coinciding with reduced low flow availability. The results indicate that changes in water availability are linked to the temporal distribution and persistence of dry anomalies. These findings can influence decisions in hydrological monitoring and water resource management strategies in the basin. 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

21 pages, 9451 KB

Open AccessArticle

Hydrogeochemical Processes Controlling Groundwater Quality and Water-Use Constraints in Semi-Arid Central Iraq

by Zainab Salah Abd Alameer, Amer A. Mohammed, Ali A. Al Maliki, Ahmed Gad, Muhammad Aufaristama and Alaa Ahmed

Hydrology 2026, 13(7), 175; https://doi.org/10.3390/hydrology13070175 - 27 Jun 2026

Viewed by 520

Abstract

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, ²²²Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen [...] Read more.

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, ²²²Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen groundwater, lake water, and municipal supply water samples were analyzed for physicochemical parameters, major ions, δ¹⁸O, δ²H, and ²²²Rn. Hydrochemical, isotopic, and water-quality assessment methods were applied to evaluate groundwater evolution, salinization, and suitability for drinking and irrigation. The waters are near-neutral, with pH values of 6.18–7.35, but are strongly mineralized. Electrical conductivity ranges from 1440 to 16,305 µS/cm, and total dissolved solids (TDS) range from 592 to 10,191 mg/L. Most samples belong to a Ca–Mg–SO₄–Cl facies, indicating sulfate- and chloride-rich hard water evolution. The highest mineralization occurs near Karbala proper and lake-influenced sites. Ion ratios and chloro-alkaline indices indicate that evaporite dissolution, gypsum/anhydrite dissolution, carbonate interaction, evaporation, and local ion exchange jointly control groundwater chemistry. Stable isotopes indicate meteoric origin with variable evaporative enrichment; however, highly saline but isotopically depleted water, particularly W8, shows that evaporation alone cannot explain salinization. ²²²Rn activities range from below detection to 11.28 Bq/L and mainly reflect local aquifer contact and degassing. High TDS, sulfate, chloride, and very high hardness limit suitability for drinking-water use. For irrigation, the sodium hazard is low, but salinity, hardness, magnesium hazard, and permeability constraints make most samples unsuitable or restricted. Management should prioritize salinity and hardness control, treatment or blending before domestic use, restricted irrigation of the least saline wells under drainage and soil-salinity monitoring, protection of less mineralized recharge zones, and long-term monitoring of lake-adjacent and agriculturally influenced wells. Full article

(This article belongs to the Special Issue Geochemical Signatures for Groundwater Resource Sustainability)

► Show Figures

Figure 1

19 pages, 3038 KB

Open AccessArticle

³H/³He Dating of Anthropogenic Tritium in a Shallow Alluvial Aquifer at Paks Nuclear Power Plant, Hungary

by László Palcsu, Andor Hajnal, István Csige, Árpád Csámer, Krisztián Baranyi, Danny Vargas and Marianna Túri

Hydrology 2026, 13(7), 174; https://doi.org/10.3390/hydrology13070174 - 26 Jun 2026

Viewed by 452

Abstract

The tritium–helium-3 (³H/³He) dating method was applied to quantify groundwater apparent ages and estimate the migration of anthropogenic tritium in the shallow alluvial aquifer surrounding the Paks Nuclear Power Plant (Hungary). Groundwater samples were collected from monitoring wells between [...] Read more.

The tritium–helium-3 (³H/³He) dating method was applied to quantify groundwater apparent ages and estimate the migration of anthropogenic tritium in the shallow alluvial aquifer surrounding the Paks Nuclear Power Plant (Hungary). Groundwater samples were collected from monitoring wells between 2013 and 2016 and analyzed for tritium and dissolved noble gases. The investigated aquifer consists mainly of highly permeable sand and gravel deposits hydraulically connected to the Danube River. Reference wells indicate apparent groundwater ages between 26 and 43 years, with an average apparent ³H/³He age of approximately 37 years. Wells located within the operational area of the power plant show apparent ³H/³He ages ranging from 1.3 to 14.1 years, reflecting the transport of tritium released during leakage events associated with damaged sewer pipelines between 2005 and 2007. The spatial distribution of apparent ages reveals heterogeneous groundwater flow paths, and highlights the influence of well-screen sampling on age interpretation. The paper demonstrates that anthropogenic tritium released from nuclear infrastructure can serve as an effective age dating method and improve conceptual models of flow dynamics in shallow alluvial aquifers. Full article

(This article belongs to the Special Issue Geochemical Signatures for Groundwater Resource Sustainability)

► Show Figures

Figure 1

18 pages, 3862 KB

Open AccessArticle

Missing Data Imputation for Reservoir Inflow Flood Discharge of Dams Based on Improved Singular Value Decomposition

by Yongjiang Chen, Kui Wang, Mingjie Zhao, Gang Liu and Jianfeng Liu

Hydrology 2026, 13(7), 173; https://doi.org/10.3390/hydrology13070173 - 26 Jun 2026

Viewed by 331

Abstract

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood [...] Read more.

Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (R_a) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal R_a value is 6. The coefficient of determination (R²) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R² (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation. Full article

► Show Figures

Figure 1

25 pages, 10062 KB

Open AccessArticle

A Novel Data-Driven Attribution Analysis of Long-Term Streamflow Changes in the Heavily Regulated, Data-Scarce Middle Reach of the Minjiang River

by Minghao Chen, Cong Li and Taihua Wang

Hydrology 2026, 13(7), 172; https://doi.org/10.3390/hydrology13070172 - 25 Jun 2026

Viewed by 388

Abstract

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir [...] Read more.

Streamflow variations in the Middle Minjiang River Basin (MMR) are vital for the flood mitigation and water resources management of the Chengdu metropolitan area which is important for the development of Southwest China. However, how climate change, Chengdu metropolitan area and Zipingpu Reservoir influence streamflow in the MMR remains unclear. Hence, we coupled the Geomorphology-Based Ecohydrological Model (GBEHM), the Physic-aware Hybrid Learning (PaHL) model and the Extreme Gradient Boosting (XGBoost) model to reproduce streamflow variations at Pengshan station—the outlet cross section of MMR—from 1980 to 2019, subsequently performing attribution analysis. Annual streamflow at Pengshan station exhibits a decreasing trend from 1980 to 2019. Coupled simulations effectively reproduce daily streamflow at Pengshan station during 35 years, with values of NSE, R² and KGE exceeding 0.96. The dominant influence of anthropogenic disturbance on daily streamflow decrease is generally steady at Pengshan station, explaining 62.3% and 430.8% of it before and after the impoundment of Zipingpu Reservoir (in 2006), respectively. Majority of the climate change’s influence is notably concentrated from June to September, suggesting a potential temporal imbalance in water resources and a threat of extreme hydrological events. Our study contributes to flood mitigation and water resources management in the MMR. Full article

► Show Figures

Graphical abstract

22 pages, 14702 KB

Open AccessArticle

Blending Precipitation Records and SEAS5 Forecasts for SPI12-Based Drought Prediction in the Lima River Basin

by Kenny Pabón Cevallos, Luis Angel Espinosa, Miguel Costa and João Pedro Pêgo

Hydrology 2026, 13(7), 171; https://doi.org/10.3390/hydrology13070171 - 25 Jun 2026

Viewed by 493

Abstract

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic [...] Read more.

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. A precipitation-only 12-month Standardized Precipitation Index (SPI12) is employed to isolate the contribution of seasonal precipitation forecasts. SPI12 is computed from hybrid 12-month accumulations combining observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). Four hybrid configurations (1 to 6 months lead time) are evaluated: 11 obs + 1 fcst, 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed from October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using regression-based metrics (e.g., Pearson correlation and RMSE) and contingency-table metrics (e.g., FAR and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of

r = 0.97

and an RMSE of approximately 0.17, alongside near-perfect categorical performance (POD = 1.00; FAR = 0.00), although these scores are partly conditioned by the shared observed accumulation window. Conversely, longer lead-time configurations exhibit degraded performance, with the 6 obs + 6 fcsts configuration showing weak or negative skill relative to climatology, indicating that 6-month lead forecasts should be interpreted with caution. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of several months in the Lima River Basin within the SPI12 framework. The proposed blending methodology provides a transparent benchmark and a technical basis for the early-warning system being developed under the RISC_PLUS project to support drought risk management in the Minho–Lima region and complement data-driven drought forecasting approaches. Full article

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

► Show Figures

Figure 1

38 pages, 25309 KB

Open AccessArticle

Integrated Flood Susceptibility and Multi-Temporal Flood Risk Prioritization in Pakistan Using Hydro-Climatic and Geospatial Indicators

by Mehjabeen Khan, Ruishan Chen and Sheheryar Khan

Hydrology 2026, 13(7), 170; https://doi.org/10.3390/hydrology13070170 - 25 Jun 2026

Viewed by 558

Abstract

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of [...] Read more.

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan. Full article

(This article belongs to the Special Issue Advances in Urban Flood Modeling, Forecasting and Early Warning)

► Show Figures

Figure 1

14 pages, 11919 KB

Open AccessArticle

Improving Daily Runoff Forecasting with VMD-VPPSO-LSTM

by Yunyi Wang, Wei Wu, Chengjun Yang, Xiaoyu Liu, Linxuan Li, Yuyue Chen and Yang Liu

Hydrology 2026, 13(7), 169; https://doi.org/10.3390/hydrology13070169 - 25 Jun 2026

Viewed by 301

Abstract

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at [...] Read more.

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at Huangtaiqiao station in the Xiaoqing River Basin, Dawenkou station in the Dawen River Basin, and Tangnaihai station in the source region of the Yellow River Basin. The proposed model achieved the best overall performance among all comparison models, with Nash–Sutcliffe Efficiency (NSE) values of 0.970, 0.962, and 0.994 and Root Mean Square Error (RMSE) values of 1.357, 0.989, and 46.804 at the three stations, respectively. Compared with VMD-LSTM, VPPSO further reduced the RMSE at all stations and maintained training-test NSE gaps below 0.006, indicating strong generalization performance. The model also achieved the lowest Peak Percent Standard Deviation (PPSD) values for high-flow events, reaching 9.03%, 14.42%, and 3.88% at the three stations, respectively. These results demonstrate that VMD-VPPSO-LSTM is a reliable and effective model for daily runoff prediction. Full article

► Show Figures

Figure 1

46 pages, 17010 KB

Open AccessArticle

Hydrographic Stratification and Pollutant Retention at Constanța Port Roadstead, NW Black Sea: Five-Layer Dissolved Oxygen Structure and a CTD-Derived Retention Index from a Single-Station Profile

by Andra-Teodora Nedelcu, Tiberiu Pazara and Manuela Rossemary Apetroaei

Hydrology 2026, 13(7), 168; https://doi.org/10.3390/hydrology13070168 - 24 Jun 2026

Viewed by 677

Abstract

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, [...] Read more.

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, five-layer water column driven by three combined forcing mechanisms: seasonal thermal stratification with an abnormally shallow Cold Intermediate Water layer (7.3–15.6 m), Danube-sourced freshwater input, and anthropogenic disturbances consistent with port and anchorage activity. A contextual hypothesis is proposed that conflict-related marine traffic intensification may contribute to observed signals, but physical measurements cannot establish causation. At the main pycnocline (7.31–15.62 m), a density difference of Δρ = 4.02 kg m⁻³ yields a maximum Brunt–Väisälä frequency of N² = 2.37 × 10⁻³ s⁻², reducing vertical eddy diffusivity by two orders of magnitude (

K z

≈ 10⁻⁶ m² s⁻¹). Physical conditions—a shallow mixed layer (~0.7–1.2 m) and strong pycnocline—support the theoretical expectation of surface-layer contaminant accumulation; however, no chemical measurements were carried out to confirm contaminant presence. All contamination inferences rely exclusively on physical proxies (turbidity, dissolved oxygen, and density gradients), and contaminant retention remains untested for lack of direct chemical evidence. A dimensional Stratification-Controlled Retention Index (SCRI = N²/Kz; units: m⁻² s⁻¹) is introduced, and its consistency with the observed hydrographic structure is demonstrated. Full article

(This article belongs to the Topic Global Water and Environmental Challenges)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Hydrology, Volume 13, Issue 7 (July 2026) – 34 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI