Journal of Hydro-environment Research最新文献

The Urmia Lake in NW Iran, the world’s second-largest hypersaline lake, has been exposed to rapid water level fall in the last two decades due to water resources mismanagement, building up of dams on inlet rivers, and the causeway construction in the lake. This human intervention has divided the lake into northern and southern parts and caused an extreme disturbance in the hydro-chemical and hydrodynamic systems, namely current direction reversal. To fully understand the current situation, water depth and lake surface and deepwater physico-chemical parameters (i.e. density, temperature, acidity, electrical conductivity, and total dissolved solids) are analysed in the wet and dry seasons of 2019. In the wet season, deepwaters have a higher density than surface waters, and the northern part of the lake contains denser water compared to its southern counterpart. The formation of density gradients creates an anticlockwise direction lake current. Deepwaters show higher electrical conductivity and total dissolved solids than surface waters in the wet season. Unlike the wet season, the lake water becomes more homogenised due to wave action and evaporation in the dry season. Our study clearly demonstrates that the water passageway of the causeway does not allow for a complete yearly balance between water and sediment exchange. Results suggest that the northern and southern parts of the lake have almost independent hydro-chemical and hydrodynamic systems. In the undeniable reality of global warming trends, anthropogenic interventions into sensible ecologic systems need to be better planned if we were to prevent the ecological disasters we now have to face.

This study aims at improving the understanding in order to optimise an aeration system for artificial destratification to control cyanobacteria growth in the reservoirs. Previous applications for artificial destratification in reservoirs were based on installations based on computational methods, where neither the effect of air bubble size and configuration nor the effect of air density in the bubble plume could be investigated. This study seeks for an optimized design with the help of experimental and numerical analyses. In order to perform experimental studies, a novel water tank enabling the heating/cooling of the water column as desired and a diffuser system were manufactured. During the experimental studies, effect of bubble size, bubble slip velocity, and other parameters of air diffuser on destratification efficiency were investigated. Based on the nondimensional parameters, a new destratification efficiency formula is obtained by the Genetic Algorithm (GA) approach. Additionaly, the hydrodynamics of the water tank during the mixing process by air diffuser was simulated via 3D numerical model and validated with experimental results. The Eulerian multiphase model with the ‘degassing’ boundary condition and k-ω turbulence model are found to be suitable for the purposes of the study. Based on the error analysis of comparisons of the model and observations, the best configuration of air diffuser is proposed, and the numerical model is found to be successful in simulating the destratification of thermally stratified water columns by air diffuser.

Water quality monitoring plays an essential role in water resource management and water governance. At present, the monitoring is commonly conducted via in-situ sampling and/or by setting up gauging stations, which can be labour intensive and costly. Recently, the possibility of monitoring water quality through remote sensing with Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors has shown great promise, with the key advantages of larger spatial coverage and possibly higher accuracy enabled by higher spectral resolution and more extensive data. Correspondingly, more advanced methods need to be established for hyperspectral analysis for water quality determination to capitalize on this wealth of information. In this study, a new method called Hierarchical Bayesian Model Aggregation for Optimal Multiple Band Ratio Analysis (HBMA-OMBRA) has been developed as a proof-of-concept for estimating Total Suspended Solids (TSS) concentrations from the hyperspectral data. The method leverages on the Bayesian ensembling of competing models because there is not a single best working model for all situations. It also encompasses a new approach called Ensemble Band Ratio Selection (ENBRAS) for the identification of best candidate band ratios (BBRs) via a set of ensembling and “bagging” procedures, followed by a modified Batchelor Wilkin’s algorithm to cluster the candidate band ratios. A laboratory investigation was conducted in the present study to measure the hyperspectral reflectance in different experiments under various environmental conditions to verify the robustness of HBMA-OMBRA. From the experimental results, six distinct clusters of candidate BBRs were identified using ENBRAS. In particular, two clusters in the red, green, and near infrared spectrum showed the largest contribution. The significance of multi-clusters provides an explanation for previously contrasting results reported in the literature and some evidence for reconciling these findings.

Best Management Practices (BMPs) are measures implemented to reduce urban runoff volume and pollution load. Determination of a cost-effective selection of BMP combinations is a challenge. In this study, an optimization model was developed to determine the optimal number, location, and type of BMPs with minimum cost and pollution load in the Majidieh catchment in Tehran, Iran. A novel framework was proposed combining the embedding technique with Response Surface Method (RSM) called “Em-RSM” in the form of a simulation–optimization (S/O) model. First, the storm water management model)SWMM(as the simulation model was linearized, and the linear programming results were used as the initial population of the genetic algorithm (GA). Then, the linearized model along with the SWMM model were alternatively used as the fitness function in the GA evolution process to increase the model run speed and results' accuracy. The results showed that the permeable pavement and infiltration trench were more effective than other BMPs because of the physical and local characteristics of the study area. It was demonstrated that the proposed model makes a considerable reduction in the model run time with acceptable accuracy in obtaining the compromise solution of the Pareto front. The proposed framework proved its effectiveness in the solution of GA-based S/O problems. It can also be applied in other case studies or optimization problems by replacing and simplifying the behavior of the simulation model in the optimization procedure.

The purposes of this study are to propose the new approach for modeling the effectiveness of low impact development (LID) practices using Hydrological Simulation Program-Fortran (HSPF)'s Surface-FTABLE (function table) and to evaluate the impacts of LID application on hydrological components and water balance. LID was simulated using Surface-FTABLE, and changes in hydrological components and water balance were analyzed. These results were compared with results simulating LID using the HSPF LID Controls Tool built in the HSPF model. Embedded within the HSPF model, the HSPF LID Controls Tool is used to design and simulate infiltration-based best management practices. Surface runoff decreased similarly for both methods using Surface-FTABLE and LID Controls Tool. For Surface-FTABLE, the infiltration in the facility was reflected in the model, so interflow, outflow and baseflow outflow increased. As a result of the water balance analysis, the results of Surface-FTABLE showed a similar bias to those of the model without LID. In contrast, the results of the LID Controls Tool showed a large bias due to uninvolved infiltration. This study showed that HSPF Surface-FTABLE is applicable to LID simulation and that it is possible to simulate the change of each element of hydrologic components reasonably.

This study investigates the determination of compound risk under the co-existence of heavy rainfall and water level rise at Alappuzha, a coastal district in Southern Kerala, India using Copulas. In the case of Alappuzha, when the combined action of rainfall and water level rise occurs, the chances of compound flooding is more in and around Vembanad Lake and lower Kuttanad regions. So, the water level and rainfall of three different locations viz. Punnmada, Cherthala, Arookutty are considered for compound flood risk analysis. A joint probability model based on Copula is used to determine the combined risk of flooding. First the marginal distributions of daily rainfall and water level data are developed for each locations and the best fit distribution is used for finding the joint probability. The three most common Archimedean copulas Gumbel–Hougaard (GH), Clayton and Frank are used to find the joint probability of rainfall and water level and the best copula for each location is also identified. Subsequently, the joint and conditional return periods of rainfall and water level are also obtained in an exercise of risk modeling. By using the digital elevation model (DEM) in a Geographic Information System (GIS) platform, the flood prone areas are calculated and represented them graphically, for specific cases of joint return period-water level combinations. This helps as an aid for administrators or policy makers to effectively perform the disaster management at Alappuzha.

Compound flooding refers to the complex interactions among oceanographic, inland (catchment) hydrological, and meteorological processes with anthropogenic factors such as land use changes due to urbanization. To-wit, the impact of higher tide levels, storm surge, and high intensity rainfall events over the inland catchment collectively may result in more extensive flooding than the individual processes acting separately. In the context of climate change and more frequent extreme weather events, coastal cities, particularly those in the ASEAN region, are increasingly vulnerable to compound flooding, yet there is no convenient and user-friendly modelling approach available that would enable the planning community and decision-makers to envision compound flooding as part of resiliency-oriented urban plan development. We addressed this gap by developing the3D Resiliency Visualisation Platform (3DRVP), within which linked features are established using Python scripts to seamless integrate a 2D mixed land use fluvial/pluvial catchment model (PCSWMM) with a 2D/3D coastal hydrodynamic model (Delft3D) to simulate the dynamics of compound floods for the assessment of coastal inundation. This platform aims to assist planners, urban design professionals, and engineers with a realistic visualization tool to picture the urban infrastructure planning alternatives as well as to facilitate the real-time operational decision-making and evacuation activation with flood control strategies. The integrated platform theory is developed first and the platform then is trialled for a developing coastal area in south Bangkok, Thailand. Similar to many cities of the global south, data availability to calibrate models is limited and as such we used a mixed methods approach to explore model accuracy. The Delft3D model was calibrated successfully using water level data from a nearby gauge in the Gulf of Thailand for Typhoon Linda. The catchment model (PCSWMM) was validated using observed flood areas as reported by the local municipality. A 100-year design rainstorm was subsequently modelled and linked with the Typhoon Linda surge levels with results indicating the combination of rainfall flooding and storm surge would increase the flooded area by 25.6% over the system components modelled individually.

The reservoirs play a crucial role in the development of civilisation as they facilitate the storage of water for multiple purposes like hydroelectric power generation, flood control, irrigation, and drinking water etc. In order to effectively meet these multiple purposes, the knowledge of the inflow in the reservoir is essential. Apart from the historical data, future prediction of the inflows is also necessary specially in context of climate change. A two-step algorithm for the prediction of reservoir inflow to enable meticulous planning and execution of daily reservoir operation keeping the historical variation of inflow in account has been proposed. The developed algorithm takes into account the patterns in the historic inflow data using the time series analysis along with the variability in the climatic patterns using the different predictors in the machine learning model. The first step uses time series model, ARIMA method to forecast the monthly inflows, which are then used as the targets in the second step for the month-wise daily forecasting of the inflows using the two types of ensemble models, namely, averaging and boosting models in machine learning. The test results show that for both the monthly models and daily models the NRMSE and NMAE values were low for the monsoon periods compared to the non-monsoon periods. The averaging ensemble models were found to perform better than the boosting ensemble models for maximum number of months. The yearly results show an error of less than 5% between actual and predicted values for all the test cases, showing the precision in the developed algorithm. Further, the uncertainty analysis shows that the prediction done using the weighted average of the different inflow scenarios performs better than the prediction against the single inflow scenario.

Diffusers are widely-used to quickly dilute effluents in receiving water bodies. This study proposed a novel diffuser that pre-mixes effluent with ambient water before discharging and that uses the swirling jet to further enhance near-field dilution. The nozzle of the diffuser was examined in two ambient flow conditions: co-flow and counter-flow that are commonly-met in the environment such as oceans due to tidal effect. Physical experiments were first conducted in co-flow on its dilution performance and hydrodynamics, using heated water as the effluent. A 3-D CFD model was developed and calibrated the co-flow scenarios, and then used to investigate the diffuser in counter-flow. The results showed that the nozzle can effectively reduce the maximum temperature rise of the effluent by about 50 % before discharging. The swirling jet from the outlet has a larger shear area, half-width and entrainment rate, enabling the effluent to be rapidly diluted to a minimum of around 10 times at x/D = 6 in co-flow, whereas the dilution for conventional nozzles is about 1 because of the potential core. The flow amplification ratio (α) decreases gradually with increasing velocity ratio in co-flow but increases with increasing velocity ratio in counter-flow. The counter-flow reduces the water drawn into the device; however, the pre-dilution effect at the outlet remains stable. The near-field dilution in counter-flow was significantly enhanced than that in co-flow. Environmental regulations at outfalls and mixing zones can be more easily met using this novel diffuser.

Aiming at resolving the grid problems caused by the inconsistent resolution requirements when simulating overland flows using the 2D shallow water equations, a novel grid generation method based on multi-resolution data fusion is developed in this work. This method is able to not only reduce the computational burden associated with uniform structured grids but also ensure the simulation accuracy of the hydrodynamic model by reproducing the so-called small-scale effect. The efficiency of the method is assessed using different cases. Theoretical and laboratory cases demonstrate that fused non-uniform structured grids can reproduce hydrographs without appreciable accuracy losses. In addition, a high simulation accuracy (NRMSE ≤ 10.40 %, R² ≥ 0.87) is achieved in the simulation of a real flood event. The performance of this method is very promising in terms of the large-scale flood simulation accuracy, and it significantly reduces the data requirements and computational burden with globally fine uniform grids.