Water

Reservoir storage capacity is a fundamental parameter for sustainable water infrastructure. Over time, progressive sedimentation reduces usable storage, intensified by climate-driven flow variations and limited flushing. This study evaluates sediment dynamics and management strategies for the shallow lowland Sĺňava Reservoir on the Váh River, Slovakia. The methodology integrates: (i) field sampling and optical granulometry to determine bed material grain-size distribution; (ii) 1D analytical formulations to establish threshold discharge for incipient sediment motion; and (iii) 2D morphodynamic modeling in HEC-RAS 2D to simulate spatial erosion and deposition patterns. Investigated scenarios included baseline natural evolution, maintenance dredging, proposed engineered sedimentation pits, and bank groins under operational flow series and extreme floods (Q₁₀₀). Results demonstrate that backwater delta aggradation is concentrated in the narrow upstream inlet reach between critical bridge profiles. Implementing targeted sedimentation pits combined with localized maintenance dredging proved to be the most effective strategy, trapping coarse bedload while mitigating scour near bridge piers. Conversely, groins provide localized flow redirection but complicate mechanical sediment removal. These findings highlight the necessity of shifting from reactive dredging to proactive morphodynamic management in regulated lowland reservoirs.

Streamflow projections under climate and land-use change rarely state which results are robust to model uncertainty. This study developed a hybrid QSWAT–ANN workflow with four verification criteria—model performance (C1), signal direction (C2), spatial ranking (C3), and reference-series sensitivity (C4)—for the Huai Luang River Basin in Northeast Thailand. We used six CMIP6 trajectories from three climate models under SSP2–4.5 and SSP5–8.5, plus a single land-use pathway to 2070. The corrected model passed C1 with an overall NSE of 0.80 and 0.79. Under ensemble precipitation increases of 10.5–13.7% and a 55.1% decrease in paddy rice toward cash crops, annual streamflow increased by 32.0–49.9%, mainly in the monsoon season, as a combined response in which the drivers were not separated. Within-trajectory Standardized Streamflow Index (SSFI) signals agreed in direction in four of six trajectories in all cases (C2 passed), and ACCESS-CM2 was the only dissenting model. The Drought Hazard Index (DHI) and Flood Hazard Index (FHI) ranks of 31 sub-basins passed C3 in three of six cases (one of four future cases), while screening identified non-overlapping robust cores of seven (DHI) and ten (FHI) sub-basins. C4 showed that index magnitudes shifted by typically 1.2–1.5 SD. Robust signal direction, seasonal structure, and spatial cores support reservoir management and sub-basin targeting, whereas absolute index magnitudes should not serve as design values. This framework is transferable to other monsoon basins.

From a hydraulic point of view, vegetation in watercourses increases flow resistance, raising water levels and the risk of flooding; its management requires a reliable computation of water surface profiles. Most drag-coefficient formulas for rigid emergent vegetation were derived under uniform flow and for staggered or random arrays, whereas linear arrays, typical of plantations and riparian reforestation, have received less attention. This experimental study assesses whether five drag-coefficient formulas can be used to compute accelerating subcritical (M2 and H2) gradually varied flow profiles in channels with linear rigid emergent vegetation. Thirty-six profiles measured in two laboratory datasets, with vegetation densities from 0.008 to 0.42, were simulated using the standard step method and evaluated with nine statistical indicators, profile-wise errors and Taylor diagrams; the sensitivity to the downstream depth and water temperature was also assessed. For densities up to 0.044, all formulas reproduced the profiles, with pooled RMSE% between 3.5% and 9.3%. For densities of 0.163 and above, the formulas derived from staggered and random arrays overestimated the depths (RMSE% of 21–40%), even within their calibrated range. A density-only formula, used with the effective flow width, gave the most consistent results over the whole range (RMSE% of 5.0% and 6.6% on the two datasets).