Journal of Hydro-environment Research最新文献

Due to climate change, the frequency and duration of meteorological drought have increased. In addition, local water supply capacity has not met water demand in many regions, which will eventually lead to serious water shortages. To mitigate the effects of drought on sustainable water use, it is necessary to understand how climate change affects regional water supply capacity and drought risk. To this end, this study evaluated the drought response capacity of regional water supply systems and assessed the comprehensive drought risk in terms of drought hazard, vulnerability, and response capacity. To avoid subjectivity in risk analysis, structural equation modeling was used to select primary indicators and probability and statistical methods were used to assign weights to the indicators. The changes in drought risk in different climate change scenarios were assessed using sensitivity analyses. The overall results indicate that the future drought risks in Gyeonggi, Gyeongsang, Chungcheong, Jeolla, and Gangwon are 18, 12, 13, 9, and 10% higher, respectively, than the current risk level. The sensitivity analyses showed that Jinju in Gyeongsang province, which has a high drought response capacity, had the largest decreasing rate in drought risk. The quantified changes in drought risk under future climate change scenarios will be useful for identifying areas with a high drought risk and making decisions about drought mitigation under climate change.

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people’s lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.

A number of viscosity and flow curve models can be used to numerically investigate the non-Newtonian behavior of fluids. Although particle size, grain size distribution and concentration play a crucial role in determining the viscosity and flow behavior of suspensions and colloidal systems, they are either ignored or considered indirectly in almost all models. We present a mathematical extension of the widely used Cross flow curve model to account for the effect of concentration and particle size in modeling viscosity and flow curves. In particular, this study takes into account a variable total number of individual particles in unit volume, which is assumed to be constant in other models. The proposed extension allows the flow curve to model suspensions that are typically shear-thinning but can also be Newtonian, or shear-thickening for at different shear rates and concentrations. These considerations provide insight into studying and designing suspensions, colloidal systems, and other complex fluid–solid interactions.

As demands for river recreational activities increases, assessing their safety has become essential to prevent accidents. The hydraulic conditions of the river critically influence the safety of in-water activities, such as sailing, paddling, and boating. Localized hazardous areas can emerge due to the spatial variability of hydraulic phenomena. This potential risk necessitates providing information about safe zones. Therefore, this study proposes a spatial river recreational index (SRRI) to assess the safety of river recreational activities over river spaces based on hydraulic factors. We reproduce the spatial distribution of the hydraulic parameters under various discharge conditions using a 3D hydrodynamic model and then estimate the SRRI by integrating all membership degrees and weights of these parameters using fuzzy synthetic evaluation (FSE). The application of the SRRI in the confluence of the Nakdong-Guemho River, South Korea, reveals that each hydraulic parameter contributes differently to safety levels, depending on discharge and morphological conditions. Specifically, the flow direction substantially decreases safety near the river confluence, whereas the water depth plays an important role in the meandering reach of the Nakdong River. Under high-flow conditions, velocity becomes a critical factor, especially for nonpowered activities (sailing and paddling/wading). The SRRI indicates that sailing is unsafe in the main flow zone and near the river confluence due to high sensitivity to discharge changes. In contrast, paddling/wading and leisure boating are less sensitive to discharge changes, allowing these activities to be partly allowable even under high-flow conditions, except in the deep-water zones of meandering reach. These results suggest that the SRRI can assist water recreational activity users in safely engaging in river recreational activities by providing spatial safety information based on various hydraulic conditions.

Numerical simulations for a large river confluence were conducted to comprehend the influences of three factors: density difference, discharge ratio, and wind shear on tributary flow dispersion. The present study focused on the confluence channel of the Nakdong River and the Yangsan Stream in South Korea, with simulation conditions selected based on realistic conditions. Numerical results revealed that tributary flow can disperse upstream under high discharge ratio conditions, which becomes stronger with density stratification. In particular, when the tributary flow is denser than the mainstream, bathymetry around the junction determines the flowing direction of the density current. Thus, understanding tributary flow dispersion under varying conditions is vital due to its influence not only downstream but also upstream of the confluence. Additionally, wind shear impact on the mixing between mainstream and tributary flow is notable but less significant than density difference or discharge ratio.

Sediment Bypass Tunnels (SBTs) are proven to be an effective measure to reduce or even stop reservoir sedimentation by bypassing sediment laden flows around reservoir dams to the downstream river reach. They are mostly used in Switzerland, Japan, and Taiwan. However, hydraulic and sedimentological operating conditions and the resistance of the invert materials against hydroabrasive erosion affect their cost-effectiveness. Hydroabrasion is a pressing issue at SBTs, other hydraulic structures and steep bedrock rivers exposed to high sediment transport rates under supercritical flow conditions. The present study was therefore conducted to address this issue by aiming at improving knowledge on abrasion mechanics and calibrating a mechanistic saltation abrasion model enhanced by Demiral-Yüzügüllü (2021). To this end, the abrasion resistance of fourteen different invert materials installed at Solis, Pfaffensprung and Runcahez SBTs in Switzerland was quantified by annual 3D laser scanning and the hydraulic conditions and sediment transport rates were regularly monitored between 2017 and 2021. The analysis of invert scans and hydraulic conditions revealed that Prandtl’s first and second kinds of secondary currents occurring in the bends and straight sections of the SBTs, respectively, and the observed abrasion patterns were strongly interrelated. The tested potassium aluminate cement and steel fibre concretes, granite, cast basalt and steel plates had better abrasion resistance against impact of sediment-laden flows compared to other materials. Sediment mineralogical composition i.e., bulk hardness relative to the invert material properties significantly affected hydroabrasion. The enhanced abrasion prediction model was calibrated with the present data and a quasi-constant abrasion coefficient of k_v = (4.8 ± 2.2) × 10⁴ was obtained. The enhanced model is well-suited for both laboratory and field scales. The present findings will contribute to the sustainable utilization and operational safety of hydraulic structures, optimization of SBT and reservoir operations regarding bypassing efficiency and reservoir lifetime and modelling of bedrock river erosion.

Urban flood vulnerability monitoring requires a large amount of socioeconomic and environmental data collected at regular time intervals. However, collecting such a large volume of data poses a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region as floods are the most devastating and recurrent phenomena in the region. The study developed a flood vulnerability index (FVI) based on socioeconomic and environmental data from a single year. This FVI was then linked to NTL data using an Adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000–2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient, and percentage of foreign nationals. The ANFIS technique performed well in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ∼ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

In canopy flows, flow resistance mainly originates from vegetation drag and depends on vegetation characteristics and flow conditions. In the present study, a series of experiments were performed in various hydraulic scenarios with high stem Reynolds numbers (2641 $\le$ Re_d $\le$ 17333) using relatively sparse rigid canopies, represented with four different dimensionless vegetation densities (0.0044, 0.0098, 0.0174 and 0.0392), on a smooth bed. A novel drag plate mechanism was developed to measure the total flow resistance due to the emergent and submerged vegetation arrays in a staggered pattern under subcritical flow conditions. Manning’s roughness coefficient and Darcy–Weisbach friction factor were adopted to represent the total flow resistance in the analyses. Simple empirical relationships based on roughness concentration and submergence ratio were derived to determine the total flow resistance parameters within a broad range of stem Reynolds numbers. Although relationships were proposed in a simple form to be used for direct practical applications, they show similar or better performance in the prediction of total flow resistance parameters than the existing equations in the literature, which require considerable computational effort. Additionally, analyses demonstrated that the results of the present study and those of similar studies regarding canopy flow resistance are in good agreement. Accordingly, the novel drag plate looks promising for measuring flow resistance due to vegetation and bed conditions similar to those in nature.

In a spillway chute flow, the upstream flow is typically non-aerated and the flow becomes self-aerated when the turbulent stresses acting next to the water surface exceeds the combined resistance of gravity and surface tension. The inception region of air entrainment is a rapidly-varied region characterised by the transition from a monophase water to two-phase air–water flow. In this contribution, field observations were conducted at large dam spillways during major flood events, with a focus on prototype data for discharges between 100 m³/s and 6,000 m³/s and Reynolds numbers between 2.6 × 10⁶ to 1.1 × 10⁸. The onset of self-aeration was a complicated three-dimensional transient process, and the dimensionless location of the inception region was a function of the Reynolds number. Surface velocities obtained with an optical technique showed that the streamwise surface velocities were close to theoretical estimates, and the streamwise surface turbulent intensities in excess of 100 %, consistent with self-aerated measurements in laboratory. The current findings yield a couple of seminal questions: (a) what do we know about prototype spillway operation during major floods? (b) how large the Reynolds number of a prototype flow needs to be truly representative of large dam spillway self-aerated flows during major flood events?