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Catchment‐scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so‐called “Runoff‐On‐Grid” approach, for embedding distributed process‐based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain‐On‐Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological‐hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non‐negligible runoff generation sources, the Runoff‐On‐Grid approach extends the surface hydrodynamic modeling to medium‐sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event‐based analysis within a High‐Performance Computing framework, the DREAM‐Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on‐site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics. Plain Language Summary In this scientific contribution, the potential of combining two different operational tools, namely distributed rainfall‐runoff and flood models, is investigated. An hindcast procedure has been used as reference to assess both the hydrological processes and the inundations at the catchment‐scale. In this context, were exploited cutting edge computational and environmental technologies, which significantly quickened the simulations and enabled a high‐fidelity reconstruction of the extreme meteorological event. According to our findings, there is merit of the proposed approach for bridging the dichotomies between the hydrological and hydrodynamic simulators. This can favor of a more comprehensive method to reduce the limitation of the standalone models. Key Points The Runoff‐On‐Grid approach integrates subsurface hydrological processes, antecedent soil moisture and soil physics in shallow water models The Runoff‐On‐Grid approach expands the capabilities of the Rain‐On‐Grid approach introducing non‐negligible runoff generation sources The DREAM‐Iber model supported by enabling technologies provides a high‐fidelty reconstruction of the 2020 Esaro flood

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.

Floodplain lakes are widespread and ecologically important throughout tropical river systems, however data are rare that describe how temporal variations in hydrological, meteorological and optical conditions moderate stratification and mixing in these shallow lakes. Using time series measurements of meteorology and water‐column temperatures from 17 several day campaigns spanning two hydrological years in a representative Amazon floodplain lake, we calculated surface energy fluxes and thermal stratification, and applied and evaluated a 3‐dimensional hydrodynamic model. The model successfully simulated diel cycles in thermal structure characterized by buoyancy frequency, depth of the actively mixing layer, and other terms associated with the surface energy budget. Diurnal heating with strong stratification and nocturnal mixing were common; despite considerable heat loss at night, the strong stratification during the day meant that mixing only infrequently extended to the bottom at night. Simulations indicated that the diurnal thermocline up and downwelled creating lake‐wide differences in near‐surface temperatures and mixing depths. Infrequent full mixing creates conditions conducive to anoxia in these shallow lakes given their warm temperatures. Key Points Diel thermal structure in a tropical floodplain lake was characterized by high‐resolution field measurements over two hydrological years State and process evaluation show that diel and seasonal stratification and mixing were simulated well by a 3‐D hydrodynamic model Diurnal heating with strong stratification and nocturnal mixing were common while mixing to the bottom was intermittent

Identifying flood‐inducing processes remains a challenge in catchment hydrology due to the complex runoff dynamics, particularly in semi‐arid regions where surface and subsurface mechanisms alternatively drive streamflow across seasons. Tracer data can help identify hydrograph sources, but they are often unavailable or lack sufficient temporal resolution. To aid process identification at the event‐scale, we developed an integrated hydrological‐hydrodynamic framework and compared multiple model hypotheses informed by hydrological signatures. We systematically tested these hypotheses through falsification, meta‐evaluation, spatial validation, and posterior diagnostics, using the semi‐arid Salsola nested catchment in southern Italy as case study. While all model structures performed well on common calibration metrics, differences emerged in spatial transferability tests and alternative diagnostic assessments. Some models, despite strong performance, exhibited inconsistent representations of internal runoff mechanisms, indicating that they achieved good results for the wrong reasons. Furthermore, the choice of routing schemes significantly influenced high‐peak estimations and overall model performance, particularly when Horton‐type overland flow was considered. This underscores the need to treat routing methods as a key component in event‐scale modeling. Our findings reveal that during consecutive storm events in the study catchment, surface processes dominate the initial stages, whereas subsurface processes become more influential in later events, providing valuable insights that may be applicable to similar semi‐arid regions. Overall, we emphasize the importance of hypothesis testing in runoff process identification, which can compensate for the absence of hydrochemical data for hydrograph separation. Additionally, our results highlight the value of a landscape‐based modeling approach for distinguishing alternative runoff generation processes. Plain Language Summary Predicting how runoff generates, sustains rivers, and triggers floods is challenging. In this study, we investigate the processes driving runoff in a semi‐arid catchment and explore how they can be identified with limited data. Rather than relying on a fixed model, we tested multiple model structures, each representing different hypotheses about runoff generation. By evaluating how well these hypotheses transfer to similar catchments, we identified the most effective ones based on both predictive performance and physical consistency. Our results show that incorporating landscape heterogeneity improves model predictions and enhances streamflow estimation. This approach provides valuable insights into flood‐inducing processes, particularly in data‐scarce regions. Key Points Surface and subsurface runoff mechanisms can alternatively dominate different flood events in semi‐arid regions A landscape‐based model structure provides enhanced performance and realistic simulated internal processes Hypothesis testing and perceptual insights can compensate for limited data when only streamflow is available

The Mirim and Patos Lagoons form the largest lagoon complex in South America. Wind is one of the dominant climatic elements of circulation and water levels in the basin. Therefore, we aimed to better understand the effects of wind on the Mirim–São Gonçalo watershed by applying the MGB hydrological model and to assess whether it would produce satisfactory results for modeling. Various tests were performed to determine the best representation of the processes involved and the observed levels. The best results were obtained with the inclusion of sub-daily wind data in the simulation and also the downstream boundary condition by using the observed water level data at the sluice dam of the São Gonçalo channel. The results showed that the model could successfully simulate the levels and demonstrated the importance of including the wind when modeling the hydrodynamic processes of large lake environments.

This study utilized MIKE 11 to quantify the spatio-temporal dynamics of water quality parameters (Biochemical Oxygen Demand (BOD5), Dissolved Oxygen (DO) and temperature) in the Long Xuyen Quadrangle area of the Vietnamese Mekong Delta. Calibrated for the year of 2019 and validated for the year of 2020, the developed model showed a significant agreement between the observed and simulated values of water quality parameters. Locations near to cage culture areas exhibited higher BOD5 values than sites close to pond/lagoon culture areas due to the effects of numerous point sources of pollution, including upstream wastewater and out-fluxes from residential and tourism activities in the surrounding areas, all of which had a direct impact on the quality of the surface water used for aquaculture. Moreover, as aquacultural effluents have intensified and dispersed over time, water quality in the surrounding water bodies has degraded. The findings suggest that the effective planning, assessment and management of rapidly expanding aquaculture sites should be improved, including more rigorous water quality monitoring, to ensure the long-term sustainable expansion and development of the aquacultural sector in the Long Xuyen Quadrangle in particular, and the Vietnamese Mekong Delta as a whole.

Flash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics. Key Points A new coupled model for simulation of entrainment, transport and deposition of vehicles driven by and interacting with flood hydrodynamics The model is used to reproduce a flash flood event that moved over 100 vehicles, with results consistent with post‐event report and survey Increasing number of floating vehicles alters flood hydrodynamics and intensifies debris‐debris and debris‐fluid interactions

Winter ice conditions in the Great Lakes play a crucial role in shaping ecological processes, shoreline dynamics, and regional weather patterns. Although atmospheric factors are widely acknowledged as the primary drivers of ice formation and duration, the influence of subsurface groundwater flow remains largely unexplored. In this study, we evaluate how spatially and temporally variable groundwater flux affects ice formation and thermal structure in Lakes Michigan and Huron, using a coupled hydrodynamic‐ice model. Simulations were conducted for the winters of 2014, 2015, and 2016—a period characterized by distinct atmospheric and ice conditions—and were validated against observed ice concentration maps and temperature profiles. Results show that groundwater enhances ice thickness during colder winters by strengthening water column stability, limiting vertical mixing, and insulating the surface layer, thus promoting thicker, longer‐lasting ice. Sensitivity analyses reveal that moderate increases in groundwater flux intensify stratification and prolong ice concentration, while an extreme, high flux (1000x) disrupts stability and reduces ice thickness. Coastal regions display more pronounced effects due to higher groundwater input, whereas offshore zones exhibit comparatively weaker responses. These findings highlight the significant role of groundwater flux in modulating ice dynamics and stratification in large freshwater systems such as the Great Lakes. This research underscores the importance of incorporating subsurface hydrology into coupled modeling frameworks to improve predictions of ice dynamics and water column stratification. Future work should focus on obtaining high‐resolution observational data on groundwater flux and ice thickness, particularly near shorelines, to further refine coupled hydrodynamic‐ice models.

Pion, L.M. and Bernardino, J.C.M., 2024. Integrated methodology for estimating maintenance dredging intervals at port berths: A case study in a Brazilian estuarine environment. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 382-386. Charlotte (North Carolina), ISSN 0749-0208. Maintenance dredging is a critical aspect of ensuring navigational safety and accessibility in port terminals worldwide. This paper addresses the urgent need for optimized maintenance dredging procedures by proposing an integrated method that combines bathymetric survey analysis and sediment transport modeling. Focusing on two berth areas within a port terminal in northeastern Brazil, referred as Areas I and II, the method aims to predict intervals between maintenance dredging campaigns. Key steps of the proposed method include processing and analyzing bathymetric data to calculate volumes above the dredging level and historical critical bed levels. A correlation curve is established between these volumes and critical levels, facilitating the estimation of bed evolution rates. Computational modeling using the Delft3D platform allows for scenario simulations, aiding in predicting future critical bed levels. Results indicate successful calibration and verification of the hydrodynamic model, with good agreement between modeled and field-measured data. Correlation curves demonstrate adequate agreement between volumes above the dredging level and critical bed levels. The results indicate the need for maintenance dredging in Area I approximately every 26 days and in Area II every 16 days on average. These intervals are consistent with historical practices over time but are shorter than ideal for terminal operation. The findings underscore the importance of the proposed method in accurately predicting maintenance dredging intervals and optimizing operational decision-making in port terminals. Future research should focus on refining correlation methodologies and overcoming practical challenges to further enhance maintenance dredging optimization in port terminals. This study contributes valuable insights into operational decision-making and underscores the importance of continued research efforts in this field.

In alluvial river systems, riparian vegetation extensively colonizes unstable bars, substantially influencing hydrodynamic characteristics and morphodynamic processes. Nonetheless, its effects on water level variation, velocity distribution, and flow diversion ratios in compound anabranching-confined river systems remain unclear. This study presents a numerical sensitivity analysis of a compound channel segment in the middle Yarlung Tsangpo River in the Tibetan Plateau. Using TELEMAC-2D, we simulated hydrodynamic processes under different vegetation scenarios (2.5–10.0 m heights, 1.5–6.0 m planting spacings), based on field observations and remote sensing images. Main results are summarized: (i) Riparian vegetation significantly elevates water levels, with the effect attenuating downstream. During a 100-yr flood, vegetated bars increased the upstream-downstream water level difference 1.71 m (unvegetated condition) to 2.44 m. (ii) Vegetation alters the cross-sectional velocity distribution and may drive cross-sectional evolution toward narrow-deep “V/W″ forms. This modulation is positively correlated with upstream inflow. In a 100-yr flood, main channel velocity rose by up to 1.4 m/s, while bar velocities dropped by up to 1.3 m/s. (iii) Flow resistance controls shift from spacing-dominated under low discharges to height-dominated at high discharges. (iv) The bar flow diversion ratio shows sigmoidal growth with discharge. This study clarifies riparian vegetation's role in affecting transitional high-altitude river hydraulics and provides a foundation for balancing ecological restoration with alpine river management. •Vegetation density dominates flow resistance at low discharges.•The height is more significant influence under high discharges.•Riparian vegetation makes the cross-section narrower and deeper.•Vegetation restrains bar diversion and enhances the advantages of the main channel.