Severe hydro-climatic variability significantly alters flood frequencies and inundation dynamics, necessitating advanced modeling for adaptive water resource management. Accurate flood inundation mapping and dynamic risk forecasting remain critical infrastructure challenges in arid and semi-arid basins subjected to significant dam-induced hydrological alterations. This study introduces an integrated eco-hydrologic and 2D hydrodynamic modeling framework coupling HEC-HMS and HEC-RAS 2D capabilities within a Geographic Information System (GIS) environment to evaluate flood dynamics under a dual-scenario for the Nesa River in Kerman, Iran. Moving beyond standard local case studies, this framework introduces a novel, non-stationary scenario-based reservoir analysis. It evaluates integrated multi-purpose reservoir operations to optimize flood control capacity while balancing downstream ecological water requirements. The analysis also dissects severe multi-hazard failure risks at the Nesa Dam spillway during extreme return periods (25, 100, and 500 years). Hydro-meteorological calibrations utilizing the SCS curve number yielded highly resilient performance metrics (NSE = 0.77; R² = 0.82). Continuous boundary condition modeling for Scenario 1 (Pre-Dam Condition) versus Scenario 2 (Post-Dam Condition) reservoir control revealed severe flood storage dynamics, with a 100-year event generating 148.73 MCM of inflow volume. By incorporating backwater effects and vegetation in mountain-to-plain transitions, flood inundation dynamics under multiple simulated flood scenarios are mapped. The 2D hydrodynamic analysis captures complex spiral channel physics and upstream backwater extensions. The results show that integrated multi-purpose reservoir operations mitigate downstream inundation areas under extreme hydrological conditions, significantly cushioning downstream reaches from catastrophic failure. However, these operations simultaneously amplify upstream inundation boundaries, threatening adjacent critical rural settlements. Furthermore, the analysis reveals that accounting for upstream backwater effects prevents the structural underestimation of flood risks in sub-mountainous reaches. This integrated approach provides a robust decision-support tool for sustainable flood management and hydraulic infrastructure design under volatile climate change.

Applying an instrumented particle [1-3], the probability density functions of kinetic energy of a coarse particle (at different solid densities) mobilised over a range of above threshold flow conditions conditions corresponding to the intermittent transport regime, were explored. The experiments were conducted in the Water Engineering Lab at the University of Glasgow on a tilting recirculating flume with 800 (length) × 90 (width) cm dimension. Twelve different flow conditions corresponding to intermittent transport regime for the range of particle densities examined herein, have been implemented in this research. Ensuring fully developed flow conditions, the start of the test section was located at 3.2 meters upstream of the flume outlet. The bed surface of the flume is flat and made up of well-packed glass beads of 16.2 mm diameter, offering a uniform roughness over which the instrumented particle is transported. MEMS sensors are embedded within the instrumented particle with 3-axis gyroscope and 3-axis accelerometer. At the beginning of each experimental run, instrumented particle is placed at the upstream of the test section, fully exposed to the free stream flow. Its motion is recorded with top and side cameras to enable a deeper understanding of particle transport processes. Using results from sets of instrumented particle transport experiments with varying flow rates and particle densities, the probability distribution functions (PDFs) of the instrumented particles kinetic energy, were generated. The best-fitted PDFs were selected by applying the Kolmogorov-Smirnov test and the results were discussed considering the light of the recent literature of the particle velocity distributions.[1] Valyrakis, M.; Alexakis, A. Development of a “smart-pebble” for tracking sediment transport. In Proceedings of the International Conference on Fluvial Hydraulics (River Flow 2016), St. Louis, MO, USA, 12–15 July 2016.[2] Al-Obaidi, K., Xu, Y. & Valyrakis, M. 2020, The Design and Calibration of Instrumented Particles for Assessing Water Infrastructure Hazards, Journal of Sensors and Actuator Networks, vol. 9, no. 3, 36.[3] Al-Obaidi, K. & Valyrakis, M. 2020, Asensory instrumented particle for environmental monitoring applications: development and calibration, IEEE sensors journal (accepted).

In the process of sediment exchange from one region of the water column to another, morphological development occurs, as does the transmission of varying sediment concentrations and flow momentum along the stream. Herein, a one-dimensional hydro-morphodynamic model is proposed for simulating water flow over a rolling bed of non-cohesive materials to understand better how water flows. Flow hydrodynamics, sediment movement, and bed growth are all considered in this simulation. The governing equations were solved using first-order accurate Harten Lax-van Leer solvers, and the fluxes at cell sides were determined using a finite volume technique based on a structured rectangular mesh. Adding geometry and bed topography to the equations in both the x and y axes may be used to convert a onedimensional model to a two-dimensional model, which is a common approach to transforming one-dimensional models into two-dimensional models. Experimental measurements are also utilized to test and assess the integrated model.

Bed particle motion as bedload transport in riverine flows is a topic of interest in scientific and engineering fields as it is responsible for erosion and sedimentation, which are essential for hydraulic structures design and maintenance [1] but also for river and basin management. The physics of particle motion as the bedload is governed by stochastic processes which interrelated various parameters and conditions (i.e., particle-particle and particle-flow interrelations). Therefore, applying physically-based or hydrodynamic modeling is not always intuitive because of the complex dynamics. In these situations, in which physics is complex, data-driven modeling approaches may yield an efficient alternative approach since it solely considers the relations among the data. Artificial intelligence models (as for data-driven approach) have offered robust predictive performance in various fields of study. In addition, for time-series and sequential forecasting, a beneficial approach is to choose a model that relates previous states to predict future events.This study contributes to developing a Long Short-Time Memory (LSTM) neural network modeling to predict the particle displacements near-threshold conditions. In order to prepare the data needed for the study, experimental tests were conducted in a hydraulic laboratory on a tilting recirculating flume with a 2000 (length) cm × 60 (width) cm dimension. Laser Doppler Velocimetry (LDV) was applied to record the flow velocity time-series upstream of the particle with 350-hertz frequency. Also, a He-Ne laser with a photomultiplier tube was used to track the particle motion [2]. Data were pre-processed with some statistical approaches for outlier detections and normalization purposes [3]. Therefore, different training and validation datasets ratios were considered, and the results were analyzed with some statistical measures (i.e., MAPE and RMSE).The proposed input-output architecture (based on the hydrodynamic forces acting on the bed particle) was a function of the future particle displacement and local instantaneous streamwise flow velocity (about 1 diameter upstream of it). Accordingly, the proposed LSTM model achieved high particle displacement prediction accuracy even for lower percent data conditions for model training. References[1] Michalis, P., Saafi, M. and Judd, M. (2012). Wireless sensor networks for surveillance and monitoring of bridge scour. Proceedings of the XI International Conference Protection and Restoration of the Environment - PRE XI. Thessaloniki, Greece, pp. 1345–1354.[2] Diplas, P., Celik, A.O., Dancey, C.L., Valyrakis, M. (2010). Non-intrusive method for Detecting Particle Movement Characteristics near Threshold Flow Conditions, Journal of Irrigation and Drainage Engineering, 136(11), pp.774-780, DOI:10.1061/(ASCE)IR.1943-4774.0000252.[3] Valyrakis, M., Diplas, P., Dancey, C.L. (2011). Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25(22). pp.3513-3524, DOI:10.1002/hyp.8228.