Next Issue
Volume 13, September
Previous Issue
Volume 13, July
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.1
Journals
Hydrology
Volume 13
Issue 8
share announcement

Hydrology, Volume 13, Issue 8 (August 2026) – 27 articles

Cover Story (view full-size image): Thousands of years ago, during the African Humid Period, people thrived in parts of the Sahara that had meager rainfall of about 250 mm/yr. By chance, three large sandstone uplifts formed the centerpiece of an extraordinary natural water system that harvested and stored rainwater. This resource helped hunter-gatherers and later herders survive. Long after the rains ended, the Garamantian civilization used underground tunnels called foggaras to bring stored groundwater to farms and towns. The chance convergence of favorable geology, hydrology, and climate created “livable niches” in a harsh environment. Although those times are long gone, water from the “Green Sahara” lives on, as groundwater pumped today supports desert agriculture in southern Libya. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
20 pages, 5434 KB  
Article
Understanding Long-Term Groundwater Storage Variability Using GRACE Data and Explainable Machine Learning
by Mehmet Ali Çelik, Adile Bilik and Yasin Paşa
Hydrology 2026, 13(8), 224; https://doi.org/10.3390/hydrology13080224 - 21 Aug 2026
Viewed by 320
Abstract
The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data [...] Read more.
The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R2 of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article
(This article belongs to the Topic Groundwater Sustainability: Innovations in Resource Management and Environmental Protection)
►▼ Show Figures

Figure 1

23 pages, 7839 KB  
Article
Regional Hydroclimatic Sensitivity of Monthly Precipitation Anomalies to ENSO in the Colombian Andes and Orinoquia
by Karen De Los Ríos, Jonathan R. Torres-Castillo, Wendy J. Rincón-Mejía, Edwin R. Celis-Montealegre, Angela Johana Riaño-Rivera and C. L. Gómez-Heredia
Hydrology 2026, 13(8), 223; https://doi.org/10.3390/hydrology13080223 - 21 Aug 2026
Viewed by 318
Abstract
El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed [...] Read more.
El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), ERA5 atmospheric fields, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies (r=0.94 in the Andes; r=0.91 in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective sample size, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was −0.374, with marked December–February and June–August responses. El Niño minus La Niña composites of column water vapor, 850-hPa moisture-flux convergence, 500-hPa vertical velocity, and Convective Available Potential Energy (CAPE) revealed seasonally heterogeneous moisture and convergence responses, but coherent positive ω anomalies over the Andes in DJF and JJA, consistent with reduced ascent. CAPE was significantly higher in MAM–SON, whereas the positive DJF difference was not statistically significant, showing that thermodynamic instability alone did not determine rainfall. Orinoquia did not exhibit a comparably consistent four-variable atmospheric signature. An elevation-stratified analysis showed a modest lowland-to-upland strengthening that plateaued above approximately 1000 m. A strictly antecedent ONI-lag model retained modest fixed-split skill in the Andes (R2=0.138) but negligible skill in Orinoquia (R2=0.003). The results support regional diagnosis, not causal or operational claims. Full article
(This article belongs to the Section Hydrology–Climate Interactions)
►▼ Show Figures

Graphical abstract

21 pages, 24128 KB  
Article
Hydrogeological Response to Low-Magnitude Seismicity: Fracture Sealing, Ground Deformation, and Lake Depletion in the Sikkim Himalaya
by Anil Kumar Misra, Vikram Gupta, Abhishek Kumar, Nikhil Raj Khatri, Rajesh Joshi, Mayank Joshi, Samir Rai and Manish Subba
Hydrology 2026, 13(8), 222; https://doi.org/10.3390/hydrology13080222 - 19 Aug 2026
Viewed by 293
Abstract
Earthquake-induced fracturing and microcrack development in subsurface strata are widely recognized as important processes influencing seepage and the hydrological behaviour of surface water bodies, particularly in tectonically active mountainous terrains. However, the hydrogeological response to repeated low-magnitude (<4) seismic events remains poorly understood. [...] Read more.
Earthquake-induced fracturing and microcrack development in subsurface strata are widely recognized as important processes influencing seepage and the hydrological behaviour of surface water bodies, particularly in tectonically active mountainous terrains. However, the hydrogeological response to repeated low-magnitude (<4) seismic events remains poorly understood. This study presents an integrated geoelectrical and remote sensing investigation of the Nagi Lake region in the Sikkim Himalaya, India, based on Vertical Electrical Sounding (VES) surveys conducted in May 2022 and March 2026, following a seismic sequence of 74 low-magnitude earthquakes recorded during February 2026. Comparative analysis of four VES profiles (VES1–VES4), supported by validatory factor analysis, reveals spatially heterogeneous changes in subsurface electrical characteristics between the two survey periods. VES1, VES2, and VES3 indicate reduced signatures of pre-existing microcracks that are consistent with sediment densification and partial sealing, whereas VES4 suggests localized development or persistence of microfractures. Because the surveys span approximately four years, these changes likely reflect the combined influence of long-term hydrogeological, environmental, and geomorphic processes, with the February 2026 seismic sequence representing one potential contributing factor rather than the sole driver. To further evaluate ground deformation, Sentinel-1A Synthetic Aperture Radar (SAR) data acquired between January 2019 and March 2026 were analysed using Persistent Scatterer Interferometric SAR (PS-InSAR). The results indicate cumulative Line-of-Sight (LOS) displacements ranging from −17.9 cm (movement away from the satellite) to +3.5 cm (movement toward the satellite) in the vicinity of Nagi Lake, reflecting localized surface deformation with millimetre-scale precision. These observations provide complementary evidence of ongoing subsurface adjustment that may promote sediment compaction and microcrack modification. Overall, the study demonstrates measurable temporal changes in the subsurface structure of the Nagi Lake area and suggests that repeated low-magnitude seismicity may contribute to subsurface restructuring alongside other environmental processes. The findings highlight the value of integrating geophysical monitoring and satellite-based deformation analysis for understanding groundwater–surface water interactions and supporting the sustainable management of vulnerable Himalayan water bodies. Full article
►▼ Show Figures

Figure 1

27 pages, 29611 KB  
Article
Multi-Scale Hierarchical Attention Ensemble Network for Fine-Grained Riverine Waste Segmentation Using UAV Multispectral Imagery
by Yohanes Fridolin Hestrio, Gatot Nugroho, Vicca Karolinoerita, Danang Surya Candra, Tri Muji Susantoro, Wismu Sunarmodo, Bagus Setiabudi Wiwoho, Ike Sari Astuti, Syarifah Hikmah Julinda Sari, I Nyoman Sutapa, Togar Wiliater Soaloon Panjaitan, Daru Setyorini, Nevenka Bulovic and Neil McIntyre
Hydrology 2026, 13(8), 221; https://doi.org/10.3390/hydrology13080221 - 18 Aug 2026
Viewed by 326
Abstract
Riverine plastic waste is difficult to detect and map accurately because debris ranges from small items to large floating clusters, and tropical rivers present challenging conditions, such as murky water, floating vegetation, and variable lighting. This study develops and benchmarks a deep learning [...] Read more.
Riverine plastic waste is difficult to detect and map accurately because debris ranges from small items to large floating clusters, and tropical rivers present challenging conditions, such as murky water, floating vegetation, and variable lighting. This study develops and benchmarks a deep learning method for pixel-level, multi-scale mapping of riverine waste from five-band UAV multispectral imagery. We deployed a drone equipped with a five-band multispectral sensor over the Brantas River in Surabaya, East Java, Indonesia, and introduce the Multi-Scale Hierarchical Attention Ensemble (MHAE), which combines three backbone networks across three image resolutions through learned scale- and backbone-attention weighting. MHAE was evaluated against 12 CNN-, transformer-, state-space-, and traditional-machine-learning-based baselines (including Random Forest, U-Net, UNet++, and DeepLabV3+) on the accompanying BrantasRiverWaste-UAV dataset (882 image tiles from a single-site, single-season orthomosaic covering approximately 0.54 km2 of river surface, labelled as water, land, organic waste, or inorganic waste), with pairwise comparisons assessed using Wilcoxon signed-rank tests with Bonferroni correction. MHAE achieved the highest pixel-level waste detection rate among the 12 evaluated models (86.76%), with a mean intersection-over-union of 78.33% (third-highest, behind UNet++ and U-Net). This work provides an initial, single-site benchmark and reference architecture for near-real-time riverine waste monitoring, and introduces the BrantasRiverWaste-UAV as, to our knowledge, one of the first five-band multispectral UAV datasets for tropical riverine waste mapping. Balanced sampling and multi-scale attention fusion can substantially improve waste-pixel detection under severe class imbalance; because the benchmark derives from a single site and season, future work should extend evaluation across additional seasons and river systems before the approach is generalised operationally. Full article
(This article belongs to the Section Hydrological Measurements and Instrumentation)
►▼ Show Figures

Figure 1

16 pages, 14487 KB  
Article
Modeling the Potential of a Roadside Two-Stage Ditch to Reduce Flooding and Erosion Risks
by Keith E. Schilling, Elliot S. Anderson, Ingrid Cintura, Betret Stanley Eustace and Antonio Arenas Amado
Hydrology 2026, 13(8), 220; https://doi.org/10.3390/hydrology13080220 - 17 Aug 2026
Viewed by 319
Abstract
Recent efforts to address flooding have explored incorporating flow-reduction capabilities into existing infrastructure. Roadside ditches have historically been viewed as an underutilized component of flood reduction, and a two-stage design has been proposed that modifies a conventional trapezoidal ditch by incorporating bench insets [...] Read more.
Recent efforts to address flooding have explored incorporating flow-reduction capabilities into existing infrastructure. Roadside ditches have historically been viewed as an underutilized component of flood reduction, and a two-stage design has been proposed that modifies a conventional trapezoidal ditch by incorporating bench insets along the main channel. While it is expected that this second stage becomes inundated during storm events, resulting in flow attenuation, the exact impacts of this design are unknown. This study quantified the impact of the two-stage design by modeling a roadside ditch corridor in eastern Iowa. An existing single-stage ditch was converted to a two-stage design, and a HEC-RAS model was constructed to investigate the ditch’s impacts for four design storms (1-year, 2-year, 5-year, and 10-year). In the modeled results, peak flow rates were reduced by 22%, 21%, 7.5%, and 4.3%, respectively, while water volume reductions were near 6%. Maximum velocities throughout the ditch corridor also decreased, with reductions spanning 32% (1-year)–45% (10-year). These results indicate that increased travel times and infiltration associated with the two-stage design provide hydrologic and hydraulic benefits by lessening flood and erosion risk. While further study is needed to verify this behavior through monitoring and modeling at other locations, our findings suggest that two-stage ditches can be a useful best management practice for the transportation community. Full article
(This article belongs to the Special Issue Urban Hydrology and Hydroclimate Resilience for Climate Change Adaptation)
►▼ Show Figures

Figure 1

18 pages, 4968 KB  
Article
Seasonal Variation in Fluorescent Dissolved Organic Matter Composition in Poyang Lake, China
by Yiling Zhong, Haiqing Liao, Fang Yang, Meng Zhang, Yuanyan Zhang, Yule Luo, Yuying Shi and Zitong Huang
Hydrology 2026, 13(8), 219; https://doi.org/10.3390/hydrology13080219 - 17 Aug 2026
Viewed by 249
Abstract
Seasonal hydrological variation can reorganize fluorescent dissolved organic matter (FDOM) in floodplain lakes; yet, its expression in Poyang Lake remains uncertain. We assessed campaign differences in FDOM composition by comparing 32 excitation–emission matrices from 16 fixed sites sampled during the dry and wet [...] Read more.
Seasonal hydrological variation can reorganize fluorescent dissolved organic matter (FDOM) in floodplain lakes; yet, its expression in Poyang Lake remains uncertain. We assessed campaign differences in FDOM composition by comparing 32 excitation–emission matrices from 16 fixed sites sampled during the dry and wet periods of 2024 using parallel factor analysis (PARAFAC), fluorescence indices, paired tests with Benjamini–Hochberg correction, and principal component analysis. Rank 4 fitted better and resolved two humic-like and two protein-like regions, although incomplete validation limited component-specific interpretation. In the wet period, C2 and combined humic-like maximum fluorescence intensity (Fmax) decreased (q = 0.00836), whereas bulk total organic carbon (TOC) and total Fmax did not differ. Mean protein-like contribution rose from 25.0% to 37.5% (q = 0.00322); the biological index increased, the humification index decreased, and the fluorescence index was unchanged. The first two principal components explained 78.11% of the variance, and an exact paired multivariate test detected an overall period difference (p = 3.05 × 10−5). No component–environment correlation survived correction or differed between periods. FDOM composition therefore underwent a campaign-specific reorganization without a corresponding change in bulk carbon, but the two-campaign design and incomplete validation preclude causal or source-specific inference. Full article
(This article belongs to the Topic Linking Agricultural–Hydrological Processes and Extreme Events Under a Changing Climate)
►▼ Show Figures

Figure 1

24 pages, 11163 KB  
Article
Multi-Objective Hyperparameter Optimization Improves the Interpretability of LSTM Rainfall–Runoff Models
by Qiuyang Tan, Jianming Shen, Youqing Wang, Moyuan Yang, Lin Zhu, Juan Zhang, Yang Liu, Zeyuan Chen, Chao Zhai and Yun Zhu
Hydrology 2026, 13(8), 218; https://doi.org/10.3390/hydrology13080218 - 14 Aug 2026
Viewed by 598
Abstract
Rainfall–runoff modeling is a key challenge in hydrological research. Despite the extensive application of long short-term memory (LSTM) networks in rainfall–runoff modeling, our understanding of the influence of different hyperparameter configurations on various hydrograph components, as well as the linkages between hydrological concepts [...] Read more.
Rainfall–runoff modeling is a key challenge in hydrological research. Despite the extensive application of long short-term memory (LSTM) networks in rainfall–runoff modeling, our understanding of the influence of different hyperparameter configurations on various hydrograph components, as well as the linkages between hydrological concepts and LSTM architectures, remains elusive. Here, we integrated Multi-Objective Particle Swarm Optimization (MOPSO) with LSTM hyperparameter optimization by targeting the root mean square error of the overall hydrograph (RMSEall), high-flow (RMSEhigh) and low-flow (RMSElow) dynamics, and water volume deviation (Dv). The MOPSO-LSTM framework was applied to the upstream catchments of the Miyun Reservoir in Beijing, China. At a lead time of 1d, the optimal solution achieved an NSE of 0.920 in the Chaohe River Basin, with a minimum RMSEall of 0.848 m3/s, RMSEhigh of 2.081 m3/s, RMSElow of 0.382 m3/s, and Dv of 0.002%. However, as the lead time increased to 3 and 7 days, the maximum NSE declined to 0.747 and 0.560, respectively, with process-related metrics deteriorating more substantially than water balance-related metrics. The Baihe River Basin performed better, with maximum NSE and KGE values of 0.949 and 0.970 at a lead time of 1d. Clear trade-offs among different evaluation objectives were further identified, particularly the competitive relationship between RMSEhigh and RMSElow, as well as the coupling between RMSElow and Dv. SHAP (Shapley additive explanation) and partial dependence plots (PDPs) were used to quantify and interpret the effects of hyperparameters on model performance, and the results showed that learning rate, number of units, and lookback window served as the most influential hyperparameters. Moreover, optimization preferences resulted in distinct hyperparameter configurations, where Dv-oriented solutions favored smaller learning rates, longer lookback windows, and larger batch sizes than RMSE-oriented solutions. Compared with the Chaohe River Basin, the larger Baihe River Basin favored LSTM configurations with longer lookback windows, more hidden units, higher learning rates, and lower dropout rates, which was associated with the hydrological memory of the catchment. Overall, this study provides a novel multi-objective LSTM optimization framework, improving the understanding of LSTM hyperparameters and offering practical guidance for hydrological prediction and water resource management. Full article
►▼ Show Figures

Figure 1

25 pages, 3803 KB  
Review
A Review of Sandbar Dynamics and River Avulsion Mechanisms
by Nihar Ranjan Sahoo, Sandeep Narayan Kundu, Muhammad Nawaz and Farha Sattar
Hydrology 2026, 13(8), 217; https://doi.org/10.3390/hydrology13080217 - 13 Aug 2026
Viewed by 416
Abstract
River avulsion, the sudden relocation of a river channel to a new course from the parent channel, is a geomorphic process with direct implications for floodplain evolution, ecosystem dynamics, and infrastructure vulnerability. This review article discusses how sandbar migration acts as a precursor [...] Read more.
River avulsion, the sudden relocation of a river channel to a new course from the parent channel, is a geomorphic process with direct implications for floodplain evolution, ecosystem dynamics, and infrastructure vulnerability. This review article discusses how sandbar migration acts as a precursor to avulsion by altering hydraulic geometry, redirecting flow paths, modifying sediment transport patterns, and affecting the development of incipient channels. The morphodynamic evolution of sandbars, influenced by sediment supply, flow regime, vegetation, and anthropogenic influences, such as dams and sand mining, plays a central role in creating avulsion. Different methods, such as field measurements, remote sensing imagery (including multispectral, SAR, LiDAR and UAV), physics-based numerical models, machine learning, and deep learning techniques, which are used to evaluate river sandbar and river avulsion, are also thoroughly evaluated for efficacy and fit for purpose. Future research should focus on combining different data sources and creating a model that understands vegetation–sediment–flow feedbacks, sediment sorting process, anthropogenic impacts and extreme climate change impacts on channel evolution. Full article
(This article belongs to the Special Issue Advancements in Measuring and Modelling River Flow Characteristics and Sediment Transport)
►▼ Show Figures

Figure 1

30 pages, 11039 KB  
Article
Comparative Performance of SCS-CN and Green-Ampt Methods in HEC-HMS Under Spatio-Temporal Rainfall Variability in a Semi-Arid Mexican Basin
by Esthela Campos Lara, Julián González-Trinidad, David Armando Contreras Solorio, Ada Rebeca Rodríguez Contreras, Hugo Enrique Júnez-Ferreira, Sandra Dávila-Hernández, Manuel Ibarra Reyes, Ana Isabel Veyna Gómez, Raúl Ulices Silva Avalos and Cruz Octavio Robles Rovelo
Hydrology 2026, 13(8), 216; https://doi.org/10.3390/hydrology13080216 - 12 Aug 2026
Viewed by 302
Abstract
Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, [...] Read more.
Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θi = θfc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m3/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m3/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m3/s), indicating a structural limitation of the physically based method under blind validation. Full article
(This article belongs to the Topic Advances in Hydrological Remote Sensing)
►▼ Show Figures

Figure 1

15 pages, 2915 KB  
Article
Hydrological Connectivity in Sandy Loam Soil Mixed with Zeolite: Insights from FFC-NMR Relaxometry Applied to Laboratory and Field Samples
by Alessio Nicosia, Gaetano Guida, Calogero Librici, Pellegrino Conte and Vito Ferro
Hydrology 2026, 13(8), 215; https://doi.org/10.3390/hydrology13080215 - 11 Aug 2026
Viewed by 340
Abstract
Using both laboratory and field samples, this paper investigates how zeolite concentration affects the hydrological connectivity of sandy-loam soil through Fast Field-Cycling Nuclear Magnetic Resonance relaxometry. Laboratory samples (LP) were prepared using four zeolite concentrations (0, 5, 10, and 15%), while field samples [...] Read more.
Using both laboratory and field samples, this paper investigates how zeolite concentration affects the hydrological connectivity of sandy-loam soil through Fast Field-Cycling Nuclear Magnetic Resonance relaxometry. Laboratory samples (LP) were prepared using four zeolite concentrations (0, 5, 10, and 15%), while field samples (FS) were collected in plots amended with the same concentrations to investigate the differences occurring during incubation time between LP and FS. For each zeolite concentration ZC, the results demonstrated that the F(T1) distribution of the FS systematically shifts towards the right compared to LP. This “scaling” effect between LP and FS was addressed using a dimensionless variable T1/σ(T1), where σ(T1) is the standard deviation of T1 considering the effects of pore size variability. The developed analysis demonstrated that the highest values of the structural connectivity index SCI correspond to ZC = 10% for LP, while ZC = 15% is necessary for FS, even if similar performance corresponds to ZC = 10%. Differences in the functional connectivity index (FCI) of LP and FS, which can be explained by environmental effects, were recognized. In conclusion, for sandy-loam soil, ZC = 10% is sufficient to improve the physical soil characteristics (highest values of structural connectivity) for both samples, while for FS, a ZC = 10% assures the minimum FCI values (the highest water-holding capacity). Full article
(This article belongs to the Special Issue State-of-the-Art on Soil Erosion and Hydrological Connectivity)
►▼ Show Figures

Figure 1

31 pages, 32326 KB  
Article
Comparing Analytic Hierarchy Process and Frequency Ratio Models for Delineating Groundwater Potential Zones in Northern Mozambique
by Larry Pax Chegbeleh, Wonder Mafuta, Gerald Albert Baeribameng Yiran, John Apambilla Akudago, Bob Alfa and Sandow Mark Yidana
Hydrology 2026, 13(8), 214; https://doi.org/10.3390/hydrology13080214 - 10 Aug 2026
Viewed by 317
Abstract
This study investigated the effectiveness and accuracy of the analytic hierarchy process (AHP) and frequency ratio (FR) models, which integrated remote sensing (RS) and geographic information systems (GIS), for delineating and validating groundwater potential zones (GWPZs) in Northern Mozambique. For both methods, six [...] Read more.
This study investigated the effectiveness and accuracy of the analytic hierarchy process (AHP) and frequency ratio (FR) models, which integrated remote sensing (RS) and geographic information systems (GIS), for delineating and validating groundwater potential zones (GWPZs) in Northern Mozambique. For both methods, six key factors influencing groundwater potential were selected from which thematic maps were generated for each. In the AHP method, the influencing factors (IFs) were subjected to a hierarchy of criteria and sub-criteria. Then, using a pairwise comparison matrix based on professional assessment and literature review, relative weights were assigned to each factor. Analysis of the AHP ultimately resulted in the integration of the various thematic layers using the weighted sum tool in ArcGIS 10.8 to produce a composite GWPZ map. In the FR method, 1026 borehole locations for the area were randomly divided into two sets: 718 boreholes (70%) were used as a training dataset, and the remaining 308 boreholes (30%) were kept as a testing dataset for validation purposes. From the training dataset, the ratio of the probability of a groundwater event (wells) occurring in a class of influencing factors to the overall probability of that event happening in the study area was calculated as the FR for that class, representing the weight assigned to each factor. The overall FR was also computed using the weighted sum tool to produce a GWPZ map. The map produced from each model delineated the area into five zones: “very low”, “low”, “moderate”, “high” and “very high”. The testing dataset was then used to validate the GWPZ maps using the field data overlay technique. The respective validation results revealed the AHP model outperformed the FR model in terms of accuracy for delineating GWPZs. Full article
(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
►▼ Show Figures

Figure 1

23 pages, 51914 KB  
Article
Effect of Urban Drainage Inlet and Building Treatment on Urban Waterlogging Simulation Under Different Storms
by Feng Wang, Ziyan Rong, Maochuan Hu, Jian Zhou, Qing Wang, Mingzhong Xiao and Bingjun Liu
Hydrology 2026, 13(8), 213; https://doi.org/10.3390/hydrology13080213 - 10 Aug 2026
Viewed by 306
Abstract
Waterlogging simulation is an important non-structural measure for flood-risk management; however, the heterogeneity of urban surfaces complicates reliable simulation. Urban drainage inlets and buildings strongly influence runoff routing, yet the effects of alternative modeling treatments remain uncertain. This study evaluated the impact of [...] Read more.
Waterlogging simulation is an important non-structural measure for flood-risk management; however, the heterogeneity of urban surfaces complicates reliable simulation. Urban drainage inlets and buildings strongly influence runoff routing, yet the effects of alternative modeling treatments remain uncertain. This study evaluated the impact of three inlet treatments and three building treatments on urban waterlogging simulation under different storms. Results show that (1) under rainfall pattern 1, the grate inlet produced 4–5.8% higher peak drainage discharge than curb-opening treatments, and point-scale water-level differences reached 0.49 m at hydraulically sensitive locations. Compared with the roof-to-drainage method, the roof-to-surface discharge method increased flood volume, flooded area, and average water depth by 43.5%, 21.3%, and 15.6%, respectively. (2) The effects of the two representation types responded differently to rainfall characteristics. Drainage inlet rankings were strongly rainfall-dependent: under rainfall pattern 2 at a 100-year return period, the hierarchy reversed, with the depressed curb-opening inlet slightly outperforming the grate inlet by 0.6%. By contrast, the building treatment methods (BTMs) ranking remained consistent across all rainfall scenarios, with the roof-to-surface discharge method producing the largest flood volume and extent regardless of rainfall pattern or return period. Overall, this study identifies urban drainage inlet and building representations as important sources of structural uncertainty, providing practical guidance for urban flood modeling and drainage planning. Full article
(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
►▼ Show Figures

Figure 1

23 pages, 41246 KB  
Article
Hourly Responses of Soil Moisture to Different Precipitation Phases Across Seasons in Alpine Regions: A Case Study from the Tanggula Mountains, Tibetan Plateau
by Han Yang, Bin Xu, Zhe Yuan, Xiaofeng Hong and Liqiang Yao
Hydrology 2026, 13(8), 212; https://doi.org/10.3390/hydrology13080212 - 6 Aug 2026
Viewed by 307
Abstract
Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist [...] Read more.
Quantifying the soil moisture (SM) response to precipitation is pivotal for predicting hydrologic resilience and ecosystem stability in fragile cold regions. This is true in alpine permafrost environments characterized by variable precipitation phases and strong seasonal freeze–thaw dynamics. However, critical knowledge gaps persist due to the scarcity of high-resolution, multi-layer in situ observations in these remote areas. Using hourly data from three sites in the Tanggula Mountains (2020–2024), this study employs an event-based analytical framework combining logistic regression and linear regression to quantify multi-layer (10–100 cm) SM responses to rain, snow, and mixed-phase precipitation across seasons. Core findings indicate the following: (1) Precipitation thresholds with 80% probability of triggering SM responses rise sharply with depth during the cold period (10 cm: 1–11 mm; 50–100 cm: often >15 mm or unreachable) but increase gradually in the warm period (10 cm: 0.4–5 mm; 50 cm: <15 mm). Mixed-phase precipitation refers to the lowest amount of precipitation (0.4–2.5 mm at 10 cm), followed by rain (1–11 mm) and snow (2–5 mm). (2) Warm-period regression slopes are consistently steeper than cold-period slopes (at 10 cm, 0.0024 vs. 0.0010 for rainfall). Mixed-phase precipitation yields the steepest slopes, approximately 50% higher than rainfall at 10 cm in the warm period (0.0037 vs. 0.0024), due to its longer duration and dual-supply mode. For lag time, cold-period values are more widely dispersed due to multiple interacting factors, while warm-period values are concentrated; only warm-period rainfall exhibits a clear monotonic increase in lag time with depth, consistent with unsaturated flow theory. (3) The quantified regression slopes, threshold values, and phase-specific efficiencies provide transferable metrics for calibrating infiltration models and evaluating frozen-ground hydrology schemes. The finding that mixed-phase events are the primary driver of deep-layer recharge, despite accounting for a smaller fraction of the total event count, has direct implications for water resource assessment in high-altitude catchments where precipitation phase composition is often oversimplified. Overall, this study moves beyond qualitative descriptions by providing quantifiable, transferable metrics that advance the mechanistic understanding of precipitation–SM coupling in alpine permafrost regions. Full article
(This article belongs to the Section Soil and Hydrology)
►▼ Show Figures

Figure 1

22 pages, 7092 KB  
Article
A Water Budget Evaluation of a Tile-Drain-Fed Irrigation Pond in the Willamette Valley, Oregon, USA
by Noah Goodwin Bain, Carlos G. Ochoa, Derek C. Godwin, Abigail Tomasek and Arshdeep Singh
Hydrology 2026, 13(8), 211; https://doi.org/10.3390/hydrology13080211 - 4 Aug 2026
Viewed by 329
Abstract
Agricultural systems face heightened risks from extreme weather events and water insecurity. Producers commonly use irrigation ponds to secure or improve crop yields. The hydrology and storage efficiency of irrigation ponds in the Willamette Valley, Oregon, USA, are not well understood. This study [...] Read more.
Agricultural systems face heightened risks from extreme weather events and water insecurity. Producers commonly use irrigation ponds to secure or improve crop yields. The hydrology and storage efficiency of irrigation ponds in the Willamette Valley, Oregon, USA, are not well understood. This study evaluated the hydrological interactions of a tile-drain-fed irrigation pond. A water balance approach was applied over the irrigation season using weather data, evaporation estimates, metered irrigation withdrawals, and bathymetry analysis for pond stage–volume estimates to quantify water budget components. Irrigation withdrawals were the largest output, with 75% of effective pond storage utilized, followed by evaporation (24.5%). Evaporation far exceeded precipitation over the same period. The unaccounted-for proportion of the water balance was negligible, indicating that net drain tile inflows and groundwater exchange had a minimal impact on seasonal irrigation water availability and seepage losses. This study provides an example for measuring water balance components and assessing water input–output relationships of an irrigation pond within a headwaters stream and catchment (<5 ha) of an important agricultural corridor in the Pacific Northwest region in the USA. The study methodology can be replicated in other similar agricultural areas with irrigation ponds worldwide. Findings from this study can be used by farmers, irrigation districts, and other stakeholders to better inform irrigation planning and water management decisions for similar site conditions. Full article
►▼ Show Figures

Figure 1

24 pages, 17503 KB  
Article
Dynamic Refinement of Temporally Static Land-Use Maps Using Satellite-Derived Moisture Signatures
by Nutchanart Sriwongsitanon, Chainarong Ophaphaibun, James Alexander Williams, Raj Mehrotra and Hubert H. G. Savenije
Hydrology 2026, 13(8), 210; https://doi.org/10.3390/hydrology13080210 - 4 Aug 2026
Viewed by 331
Abstract
Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference [...] Read more.
Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) with unsupervised K-means clustering and a temporally consistent refinement strategy. Multi-temporal NDVI (23 composites year−1) and NDII (46 composites year−1) data from 2010–2021 were used to derive spectral clusters and aggregate them into five land use classes using percentile-based temporal signatures and RMSE-based similarity with Land Development Department (LDD) data. To reconcile discrepancies between dynamic satellite observations and static reference datasets, a refinement procedure combining spatial agreement and temporal similarity was applied to reassign misclassified pixels. Initial classifications achieved Overall Accuracies (OA) of 57.35% for NDII and 51.27% for NDVI, increasing to 87.28% and 86.24% after refinement, with Kappa coefficients of 0.82 and 0.81, respectively. NDII consistently outperformed NDVI, highlighting the value of moisture-sensitive indices for distinguishing vegetation classes in tropical environments. The modular Python-based version 3.11 implementation ensures reproducibility and transferability, providing a robust and scalable framework for LULC classification in dynamic landscapes. Full article
(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)
►▼ Show Figures

Figure 1

24 pages, 1583 KB  
Article
Comparative Evaluation of Machine Learning Algorithms for Predicting Soil Wetting Front Dynamics Under Drip Irrigation System
by Oluwaseun Temitope Faloye, Oluwaseyi Matthew Abioye, Abiodun Afolabi Okunola, Olusegun K. Abass, Peter Pelumi Ikubanni, Natdanai Sinsamutpadung, Laemthong Laokhongthavorn and Viroon Kamchoom
Hydrology 2026, 13(8), 209; https://doi.org/10.3390/hydrology13080209 - 3 Aug 2026
Viewed by 262
Abstract
Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, [...] Read more.
Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, irrigation variables, and different soil textures. This study evaluated four machine learning algorithms—Linear Support Vector Machine (Linear SVM), Medium Gaussian Support Vector Machine (Medium Gaussian SVM), Matern 5/2 Gaussian Process Regression (GPR), and Boosted Tree Regression—for predicting wetted width and wetted depth in sand and sandy loam soils. Model inputs included emitter discharge, irrigation duration, and selected soil hydro-physical and chemical properties. Models were developed using a 70% training dataset and validated with the remaining 30%. The Matern 5/2 GPR achieved the highest training accuracy for wetted width (R2 = 0.99; RMSE = 0.74) and wetted depth (R2 = 0.98; RMSE = 0.90), but validation errors increased to RMSE values of 2.27 and 3.84, respectively. Medium Gaussian SVM yielded the lowest validation RMSE (2.11) for wetted width, whereas Boosted Tree Regression achieved the best wetted depth prediction (RMSE = 2.11; MAE = 1.69). These findings demonstrate the importance of model-specific selection for reliable irrigation management. Full article
►▼ Show Figures

Figure 1

42 pages, 13504 KB  
Article
Climate Change and Irrigation Effects on Hydrology and Crop Yield in the Geba Watershed, Tigray Region, Northern Ethiopia
by Adane Weldengus Meresa, Muuz Gebretsadik Gebremariam, Anthony Lehmann and Mostafa Jafari
Hydrology 2026, 13(8), 208; https://doi.org/10.3390/hydrology13080208 - 3 Aug 2026
Viewed by 469
Abstract
Climate change and irrigation expansion are expected to substantially alter hydrological processes and agricultural productivity in the semi-arid watersheds of northern Ethiopia; however, their combined impacts remain insufficiently quantified. This study evaluated the effects of future climate change and irrigation management on watershed [...] Read more.
Climate change and irrigation expansion are expected to substantially alter hydrological processes and agricultural productivity in the semi-arid watersheds of northern Ethiopia; however, their combined impacts remain insufficiently quantified. This study evaluated the effects of future climate change and irrigation management on watershed hydrology and crop yield in the Geba watershed using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated using observed daily streamflow data for the 2006–2020 period and driven by an ensemble of five bias-corrected CORDEX Africa regional climate models (RCMs) under the RCP 4.5 and RCP 8.5 scenarios for the mid-century (2046–2060) and late-century (2086–2100) periods. Two agricultural management systems, namely rainfed and irrigation-rainfed integrated management, were evaluated. Model performance was satisfactory for streamflow simulation, with NSE values of 0.56 and 0.50 and KGE values of 0.64 and 0.54 during calibration and validation, respectively. The results indicate a progressive shift toward an evapotranspiration-dominated hydrological regime under future climate conditions. Under rainfed management, surface runoff and evapotranspiration increased by up to 60% and 30%, respectively, whereas groundwater recharge and lateral flow declined substantially. Irrigation scenarios intensified hydrological stress by reducing percolation, lateral flow, and water yield by up to 80%, 60%, and 55%, respectively. Statistical analyses revealed that climate forcing, management type, and their interactions significantly affected hydrological responses (p < 0.001), with emission pathways representing the dominant driver of variability. Crop responses varied considerably among management systems and crop types. Rainfed maize and wheat exhibited moderate yield increases under mid-century conditions, whereas teff consistently showed negative responses under most climate scenarios, indicating high vulnerability to warming and moisture stress. Under irrigation management, most crops experienced substantial yield reductions during late-century periods, although tomatoes showed localized gains under high-emission scenarios. Overall, the findings demonstrate that irrigation expansion alone may not provide sustainable adaptation under increasing climate stress because it intensifies evapotranspiration and reduces groundwater recharge. Integrated watershed management, climate-resilient crop selection, efficient irrigation practices, and soil-moisture conservation strategies are therefore essential for sustaining agricultural productivity and water availability in semi-arid Ethiopian watersheds. Full article
(This article belongs to the Topic Linking Agricultural–Hydrological Processes and Extreme Events Under a Changing Climate)
►▼ Show Figures

Graphical abstract

20 pages, 3271 KB  
Article
Serendipity in Settings and Hydrologic Processes Helped People Survive Extreme Environments of the Sahara Desert
by Franklin Schwartz and Ganming Liu
Hydrology 2026, 13(8), 207; https://doi.org/10.3390/hydrology13080207 - 3 Aug 2026
Viewed by 552
Abstract
This paper explores the intricate relationship between novel paleo-hydrological settings and the sustainability of ancient human societies in the Sahara Desert, focusing on the sandstone massifs of Tassili n’Ajjer, Tadrart Acacus, and Messak Settafet. While this region is currently hyper-arid, archeological evidence reveals [...] Read more.
This paper explores the intricate relationship between novel paleo-hydrological settings and the sustainability of ancient human societies in the Sahara Desert, focusing on the sandstone massifs of Tassili n’Ajjer, Tadrart Acacus, and Messak Settafet. While this region is currently hyper-arid, archeological evidence reveals a history of significant human settlement facilitated by the African Humid Period (AHP). The core of the research is the idea that the natural geological and hydrogeological settings worked to magnify rainfall in a manner that is analogous to modern techniques in water systems engineering. Serendipitous features of geology, structural settings, and stream networks are presented, along with illustrative calculations to suggest how this system functioned as an accidental rainwater harvesting system, concentrating runoff into conveniently located lakes. On the Messak Settafet, the archaeologic evidence points to a rising water table and more robust groundwater flow as runoff infiltrated. We conceptualize this behavior as a managed aquifer recharge system. This natural system worked effectively by storing ephemeral surface water in a sandstone aquifer, shielded from the high evaporation rates of the Sahara. These “natural technologies” created perennial water sources such as lakes, ponds, and springs that supported hunter-gatherers and pastoralist societies. Long after the end of the Holocene AHP, the Garamantian Empire arose with the help of qanat technology that was able to produce the stored groundwater. This paper illustrates how an unlikely array of components worked to create natural technologies able to provide “livable niches.” These findings offer instructive lessons for modern sustainability, demonstrating how integrated landscape management can secure water resources in water-stressed environments. Full article
(This article belongs to the Section Surface Waters and Groundwaters)
►▼ Show Figures

Graphical abstract

40 pages, 38022 KB  
Article
Validation of Downscaled and Bias-Corrected WorldClim 2.1– CRU-TS v4.09 Climate Dataset for Hydrological Modeling in a Semi-Arid Ecotonal Catchment of Central South Africa
by Kassaye Hussien and Yali E. Woyessa
Hydrology 2026, 13(8), 206; https://doi.org/10.3390/hydrology13080206 - 28 Jul 2026
Viewed by 976
Abstract
Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and bias-corrected from CRU-TS v4.0 using WorldClim 2.1 climatology, against observed meteorological records [...] Read more.
Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and bias-corrected from CRU-TS v4.0 using WorldClim 2.1 climatology, against observed meteorological records within the semi-arid C5 Secondary Drainage Region (C5 SDR; comprising the Riet and Modder River catchments) in central South Africa for the period 1950–2023. Precipitation, maximum temperature (TMAX), and minimum temperature (TMIN) were assessed using statistical performance evaluation metrics, scatter and residual analyses, Innovative Trend Analysis (ITA), Rescaled Adjusted Partial Sums (RAPS), and extreme-event evaluation based on the 95th-percentile threshold. The results showed strong agreement between observed and gridded precipitation records, with correlation coefficients ranging (R) from 0.78 to 0.90 and Nash–Sutcliffe Efficiency (NSE) values between 0.61 and 0.90. Temperature datasets exhibited similarly good performance, with TMAX showing stronger agreement than TMIN. ITA and RAPS analyses demonstrated that the dataset successfully reproduced long-term climatic trends, hydroclimatic regime shifts, and interannual variability observed in station records. Performance varied spatially, with the strongest agreement occurring at lower-elevation stations and comparatively lower performance at stations influenced by localized convective rainfall and topographic variability. Extreme-event analysis revealed that although the dataset effectively reproduced the timing and occurrence of high-rainfall years (R2 = 0.974–0.997), it systematically underestimated the magnitude of extreme precipitation events, with percent bias values ranging from −5.5% to −21.0%. In contrast, extreme temperature events were reproduced with very high accuracy and minimal bias. Overall, the WC2.1– CRU-TS v4.09 dataset provides a reliable climatic baseline for hydroclimatic assessments in the C5 SDR. However, caution is required when applying the dataset to analyses sensitive to localized precipitation extremes. The results provide confidence in the use of this dataset for climate characterization, drought assessment, hydrological modeling, ecosystem service evaluation, and future climate-change impact investigations in data-scarce semi-arid environments. Full article
►▼ Show Figures

Figure 1

16 pages, 7616 KB  
Article
Spatiotemporal and Future Changes in Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China Under Climate Warming and Vegetation Greening
by Yujiao Liu, Mengzhu Liu, Borui Li and Hongwei Pei
Hydrology 2026, 13(8), 205; https://doi.org/10.3390/hydrology13080205 - 28 Jul 2026
Viewed by 393
Abstract
The water use efficiency (WUE) in North China is undergoing rapid changes due to climate warming and vegetation “greening”, significantly impacting the ecosystem’s carbon and water cycles. Existing research lacks quantitative analysis of WUE or an understanding of future trends. This study selected [...] Read more.
The water use efficiency (WUE) in North China is undergoing rapid changes due to climate warming and vegetation “greening”, significantly impacting the ecosystem’s carbon and water cycles. Existing research lacks quantitative analysis of WUE or an understanding of future trends. This study selected the rapidly greening Agro-Pastoral Ecotone of Northern China (APENC) as a case study, utilizing linear regression, Hurst index analysis, and residual analysis to analyze the past and future changes and driving mechanisms of WUE. The results indicated that: (1) The multi-year (2001–2023) annual mean WUE in the APENC spatially ranged from 0.32 to 2.50 g C kg−1 H2O. (2) Gross primary productivity (GPP), evapotranspiration (ET), and WUE showed significant increasing trends of 10.22 g C m−2 yr−2, 5.62 kg H2O m−2 yr−2, and 0.01 g C kg−1 H2O yr−1, respectively. (3) Precipitation had highly positive impacts on GPP and ET, while non-climatic factors (land use, human activities, etc.) explained 62% of WUE variations in the APENC, and energy conditions (air temperature and solar radiation) were not the decisive factor of WUE. (4) The Hurst exponent of WUE indicates that WUE in the APENC region generally exhibits anti-persistent behavior. In terms of future trends, WUE is projected to shift from rising to declining in 58.9% of the region, while 28.5% is expected to continue increasing. Full article
(This article belongs to the Topic Ecohydrology and Water Resources Sustainability, 2nd Edition)
►▼ Show Figures

Graphical abstract

14 pages, 2457 KB  
Article
Multivariate Characterization of Hydrochemically Similar Groundwaters: Resolving Hydrochemical Structure and Process-Related Variability
by Riccardo Aigotti, Eugenio Alladio, Alberto Asteggiano and Claudio Medana
Hydrology 2026, 13(8), 204; https://doi.org/10.3390/hydrology13080204 - 28 Jul 2026
Viewed by 265
Abstract
Groundwater systems sharing similar major-ion facies may still differ in their hydrochemical organization and mineralization pathways, particularly in structurally complex aquifer settings. This study evaluated multivariate chemometric approaches for investigating two hydrochemically similar groundwater systems (MAJA and MAJA2) examined within the regulatory framework [...] Read more.
Groundwater systems sharing similar major-ion facies may still differ in their hydrochemical organization and mineralization pathways, particularly in structurally complex aquifer settings. This study evaluated multivariate chemometric approaches for investigating two hydrochemically similar groundwater systems (MAJA and MAJA2) examined within the regulatory framework for natural mineral water recognition. The dataset consisted of a 13-month monitoring campaign complemented by an independent multi-year validation dataset. Hydrochemical variables were organized into chemical and process-related blocks, including major ions, physicochemical parameters, D’Amore indices, and mineral saturation indices. SIMCA was applied to evaluate the intra-class hydrochemical structure, and OPLS-DA was used to investigate predictive and orthogonal sources of variability. Model robustness and parameter reproducibility were assessed using jackknife resampling, Leave-One-Month-Out cross-validation, repeated double cross-validation, and permutation testing. SIMCA identified PC1 as the only consistently reproducible latent component across resampling iterations. An exploratory Structural Response Coefficient (Rj) was introduced as a model-derived descriptor integrating explained and residual variance within the SIMCA model. OPLS-DA models showed stable class-related latent structures under nested validation conditions. Electrical conductivity, sulphate, potassium, SI_gypsum, SI_halite, and D’Amore index A were the variables most consistently associated with discriminant variability. Stable isotope data indicated a common meteoric origin and similar recharge conditions for both systems. The results illustrate how multivariate chemometric analysis, combined with stability-oriented validation procedures, may aid the interpretation of hydrochemical variability in compositionally similar groundwater systems. Full article
(This article belongs to the Topic Advances in Groundwater Science and Engineering)
►▼ Show Figures

Figure 1

18 pages, 2297 KB  
Article
Model Predictive Control-Based Hydrodynamic Regulation Framework for the Lower Ganjiang River
by Sufen Zhou, Xinming Zhang, Zhiwen Huang and Limo Tang
Hydrology 2026, 13(8), 203; https://doi.org/10.3390/hydrology13080203 - 27 Jul 2026
Viewed by 219
Abstract
The Lower Ganjiang River is a multi-branch delta with highly uneven spatial and temporal flow distribution, and conventional static diversion or threshold-based operation fails to stabilise the water level or optimise flow allocation under varying inflows. This study develops a hydrodynamic regulation framework [...] Read more.
The Lower Ganjiang River is a multi-branch delta with highly uneven spatial and temporal flow distribution, and conventional static diversion or threshold-based operation fails to stabilise the water level or optimise flow allocation under varying inflows. This study develops a hydrodynamic regulation framework that couples an improved integral time-delay model and model predictive control (MPC). A nonlinear state-space equation is constructed using a quadratic storage–water level relationship and rolling optimisation is solved with CasADi-IPOPT to minimise water-level tracking error, discharge deviation and control effort. The framework is validated offline against MIKE21 simulations for three historical flow scenarios (September 2016, February 2017 and March 2018). Under these scenarios, the Waizhou water level is maintained at 15.5 ± 0.2 m, daily water level variation is limited to ≤0.5 m/d, and the diversion ratio deviation is ≤5%. Compared with the natural state, water level fluctuation is reduced by 21.3% (September 2016 storage scenario). The proposed MPC framework effectively alleviates the spatiotemporal hydrodynamic imbalance of the Lower Ganjiang River, showing satisfactory model accuracy, constraint compliance, and engineering applicability, and offers a promising approach for advanced regulation of complex multi-branch river networks. Full article
(This article belongs to the Section Hydrological Measurements and Instrumentation)
►▼ Show Figures

Figure 1

27 pages, 13001 KB  
Article
Hydroclimatic Variability and Floodplain Wetland Dynamics in the Magdalena River: A Case Study of Zambrano, Colombia
by Ana Carolina Torregroza-Espinosa, Juan Camilo Restrepo, Rodney Correa-Solano, David Alejandro Blanco-Álvarez and Laura Salas Cantillo
Hydrology 2026, 13(8), 202; https://doi.org/10.3390/hydrology13080202 - 25 Jul 2026
Viewed by 369
Abstract
Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector [...] Read more.
Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article
►▼ Show Figures

Figure 1

22 pages, 5169 KB  
Article
Enhancing Daily Runoff Prediction via Uniform Design and Meta-Learning Integrated Hyperparameter Optimization Embedded in Transformer
by Wenxue Wang, Liuyang Li, Donghui Su, Xin Zhang, Haibin Tong, Tiantian Shao and Jiaxin Fan
Hydrology 2026, 13(8), 201; https://doi.org/10.3390/hydrology13080201 - 25 Jul 2026
Viewed by 273
Abstract
Accurate runoff prediction is an essential foundation for water resource management, flood prevention, and drought warning. Despite the superior performance of deep learning models in runoff prediction, the high-dimensional hyperparameter optimization limits their widespread application. To address this challenge, this study proposed a [...] Read more.
Accurate runoff prediction is an essential foundation for water resource management, flood prevention, and drought warning. Despite the superior performance of deep learning models in runoff prediction, the high-dimensional hyperparameter optimization limits their widespread application. To address this challenge, this study proposed a hyperparameter optimization strategy that integrated Uniform Design (UD) and Meta-Learning (ML) within the Transformer framework (UD-ML-Transformer) for daily runoff prediction. Performance of the proposed model was systematically evaluated against five benchmark models, including the UD-Transformer, Particle Swarm Optimization (PSO)-Transformer, and three Receptance Weighted Key Value (RWKV)-based models (PSO-RWKV, UD-RWKV, and UD-ML-RWKV), using hydroclimatic data spanning 1980 to 2014 from the Rio Pueblo de Taos watershed in USA. Results showed that the UD-ML-Transformer model performed the best in both prediction accuracy and peak flow, with the highest Nash-Sutcliffe Efficiency (NSE) of 0.906, and the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) of 0.004, 0.062, and 0.034, respectively. The UD-Transformer ranked second in performance, followed by the PSO-Transformer. The integrated UD-ML hyperparameter optimization strategy also improved the performance of RWKV-based models. Compared with the PSO-RWKV and UD-RWKV models, the UD-ML-RWKV model exhibited an NSE improvement of 0.45–7.65% and an RMSE reduction of 1.47–21.18%, respectively. Moreover, cross-watershed validation conducted in the Ford River watershed, USA, also demonstrated the satisfactory performance of the proposed UD-ML-Transformer model, with the highest NSE of 0.890, and the lowest MSE, RMSE, and MAE of 0.088, 0.296, and 0.141, respectively. These findings highlight the superiority of integrating UD and ML for hyperparameter optimization in runoff forecasting. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
►▼ Show Figures

Figure 1

26 pages, 22497 KB  
Article
Evaporative Water Consumption and Heat Redistribution Under Pumped-Storage Hydropower Operation in an Arid Region
by Jinhan Wang, Xinjun Yan, Shaolei Wang, Kewu Han, Kebin Shi and Dexin Zhao
Hydrology 2026, 13(8), 200; https://doi.org/10.3390/hydrology13080200 - 24 Jul 2026
Viewed by 288
Abstract
Pumped-storage hydropower (PSH) can modify reservoir evaporation in arid regions by altering water-level dynamics, surface-area exposure, and thermal exchange between reservoirs. This study quantifies operation-induced evaporation changes at the Fukang PSH station in Xinjiang, China, using a one-dimensional lumped hydrodynamic–thermal model driven by [...] Read more.
Pumped-storage hydropower (PSH) can modify reservoir evaporation in arid regions by altering water-level dynamics, surface-area exposure, and thermal exchange between reservoirs. This study quantifies operation-induced evaporation changes at the Fukang PSH station in Xinjiang, China, using a one-dimensional lumped hydrodynamic–thermal model driven by hourly station observations and ERA5 reanalysis for 2024. A four-scenario factorial design separates thermal, surface-area, and interaction effects within a unified water energy framework. Under station forcing, fully coupled operation reduces annual system-scale evaporation from 135.36 × 104 m3 to 115.45 × 104 m3, corresponding to a net reduction of 19.91 × 104 m3 (14.7%). Energy-budget analysis identifies advective heat transport as the main pathway linking dispatch, reservoir thermal evolution, and evaporation response, with annual cumulative values of +205 TJ in the upper reservoir and −333 TJ in the lower reservoir. Dispatch-regime experiments further show that stronger exchange-flow operation does not necessarily increase evaporation reduction: the low, baseline, and enhanced schedules produce system-scale net changes of 37.70 × 104 m3, 19.91 × 104 m3, and −3.41 × 104 m3, respectively. These results indicate that evaporation effects in arid-region PSH systems depend on the timing of surface-area exposure relative to local evaporative demand, rather than on exchange-flow magnitude or operating duration alone. Full article
(This article belongs to the Section Water Resources and Risk Management)
►▼ Show Figures

Figure 1

15 pages, 6783 KB  
Article
Frequency-Dependent Groundwater Responses to Canal Regulation and Extreme Rainfall in the Huaibei Plain
by Zhaokai Wang, Hongwei Yuan, Jiwei Yang, Tao Shen and Youzhen Wang
Hydrology 2026, 13(8), 199; https://doi.org/10.3390/hydrology13080199 - 23 Jul 2026
Viewed by 321
Abstract
Groundwater levels in gated agricultural drainage networks respond to canal-stage changes, rainfall, antecedent storage, and changing operating conditions. We examined groundwater and surface-water records from the Chezegou Watershed, Huaibei Plain, China (2019–2024), using analytical solutions of the linearized Boussinesq equation. Groundwater was measured [...] Read more.
Groundwater levels in gated agricultural drainage networks respond to canal-stage changes, rainfall, antecedent storage, and changing operating conditions. We examined groundwater and surface-water records from the Chezegou Watershed, Huaibei Plain, China (2019–2024), using analytical solutions of the linearized Boussinesq equation. Groundwater was measured mostly at intervals of about five days, and analyses used the original observation dates. Using the half-power criterion |Z|2 = 1/2 and hydraulic diffusivities of 5.27 × 103–1.05 × 104 m2 d−1, cutoff periods were 56.1–111.7 d at 150 m and 399.1–794.1 d at 400 m; the half-power distance for a 30 d cycle was 77.7–109.7 m. The record also includes a 106 mm storm on 12 July 2020. Groundwater depth at J5 (490 m from the canal) decreased from 2.23 to 0.54 m in 48 h, a 1.69 m water-level rise, while J9 (1020 m) rose by 1.79 m over five days. These observations show a rapid shallow-groundwater head response, although water-level records alone do not separate vertical recharge from hydraulic-pressure transmission. Canal influence depends on forcing duration and aquifer properties, while rainfall responses also reflect lateral boundaries and the shrink-swell behavior of Shajiang black soil. The calculated time-distance relations provide site-specific reference values for canal operation. Full article
►▼ Show Figures

Figure 1

36 pages, 45114 KB  
Article
Groundwater Vulnerability Assessment Using an Integrated GIS-Based DRASTIC, Land-Use, and Expert Elicitation Framework in Southern Egypt
by Mohamed El-Sayed El-Mahdy, Sally Sayed Saad, Ibraheem A. H. Yousif, Mohamed Ahmed Shahba and Abd-Alrahman S. Ahmed
Hydrology 2026, 13(8), 198; https://doi.org/10.3390/hydrology13080198 - 23 Jul 2026
Viewed by 427
Abstract
Groundwater vulnerability refers to an aquifer’s susceptibility to contamination based on natural hydrogeological properties, including geology, soil, topography, and unsaturated zone characteristics. In low-recharge arid systems, limited recharge reduces dilution and flushing, allowing contaminants introduced through anthropogenic activities to persist over time. This [...] Read more.
Groundwater vulnerability refers to an aquifer’s susceptibility to contamination based on natural hydrogeological properties, including geology, soil, topography, and unsaturated zone characteristics. In low-recharge arid systems, limited recharge reduces dilution and flushing, allowing contaminants introduced through anthropogenic activities to persist over time. This study assesses groundwater vulnerability in El-Farafra, El-Kharga, and Tushka using a GIS-based DRASTIC approach, enhanced with a Land-Use DRASTIC model to incorporate human activities. Parameters, including the depth-to-water table, net recharge, aquifer media, soil media, topography, the vadose zone, and hydraulic conductivity, were spatially analyzed to generate vulnerability indices. Sentinel-2 imagery was used for land-use classification. In addition, expert elicitation from twenty hydrogeology specialists provided alternative parameter weightings, which were compared with the standard DRASTIC weights. Results show that incorporating land use and expert-based weights refines vulnerability patterns, particularly in agricultural, urban, and industrial zones. El-Farafra exhibits the highest vulnerability due to intensive land use and hydrogeological conditions, El-Kharga shows moderate vulnerability, and Tushka shows lower vulnerability, where recharge from Lake Nasser enhances dilution and reduces contaminant persistence. The study highlights the importance of integrating land-use information and expert knowledge to improve vulnerability assessment in data-scarce arid environments and supports improved groundwater management strategies. Full article
(This article belongs to the Section Surface Waters and Groundwaters)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-27
Previous Issue
Next Issue
Back to TopTop