Simulated Water Levels Research Articles

Assessing the Impact of Extensive Severe Cyclonic Storm Fani on the Coastal Communities of Bangladesh: A Case Study

This research article presents a numerical simulation of the extensive severe cyclonic storm Fani and its impact along the coast of Bangladesh, which made landfall in the coastal region of Odisha, India, on May 3, 2019. A semi-implicit finite difference method in Cartesian coordinates was employed in this study to solve vertically integrated shallow water equations. The approach allowed for effective forecasting of the storm Fani's impact on the region of choice. Our considered physical domain is discretized with high-resolution gird to cover all the big and small offshore islands. The model predicted water levels for a total of sixteen coastal stations of the Bay of Bengal along the coast of Bangladesh (from 2 May to 03 May 2019, at 3-h interval). Simulated water levels are found closely co-related to the reported data. The simulation results demonstrate that the semi-implicit finite difference method is an effective tool for simulating the storm surge and flooding caused by any severe cyclonic storm. The study also shows that the storm surge and flooding caused by Fani were significant, with a maximum surge height of over 5 meters in some areas. The simulated results provide insights into the spatial and temporal evolution of the storm surge and flooding, which can be useful for designing and implementing appropriate disaster management strategies in the affected regions. Overall, this research article contributes to the scientific understanding of the behavior of severe cyclonic storms and the associated storm surge and flooding, and provides a valuable tool for policymakers and stakeholders to develop and implement effective disaster management strategies in cyclone vulnerable coastal regions.

Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter

Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accurate water level simulation in complex river networks. The EnKF-based data model was tested on simulated water level data from a river network hydrodynamic model and optimized through parameter analysis. It was then applied to a real mountainous single-channel river and plain river network and compared with a data assimilation model based on the extended Kalman filter (EKF). The results showed that the EnKF-based model, with a medium ensemble sample size of 100–150, normal observation noise of 0.0001–0.01 m, and a high standard deviation of 0.01–0.1 m, outperformed the EKF-based model, with a 49% reduction in simulation errors, a 45% reduction in calculation cost, and a 43% reduction in filtering time. Furthermore, the EnKF-based data assimilation model predicted the water level in the plain river network better than the mountainous single-channel river. Around 5 to 8 h were required for data assimilation; afterwards, the model could make accurate predictions covering 20 to 30 h. The EnKF-based data assimilation model offers a potential solution for water level predictions in river networks.

An integrated simulation–optimization framework for assessing environmental flows in rivers

The present study proposes an integrated simulation–optimization framework to assess environmental flow by mitigating environmental impacts on the surface and ground water resources. The model satisfies water demand using surface water resources (rivers) and ground water resources (wells). The outputs of the ecological simulation blocks of river ecosystem and the ground water level simulation were utilized in a multiobjective optimization model in which six objectives were considered in the optimization model including (1) minimizing losses of water supply (2) minimizing physical fish habitat losses simulated by fuzzy approach (3) minimizing spawning habitat losses (4) minimizing ground water level deterioration simulated by adaptive neuro fuzzy inference system(ANFIS) (5) maximizing macroinvertebrates population simulated by ANFIS (6) minimizing physical macrophytes habitat losses. Based on the results in the case study, ANFIS-based model is robust for simulating key factors such as water quality and macroinvertebrate’s population. The results demonstrate the reliability and robustness of the proposed method to balance environmental requirements and water supply. The optimization model increased the percentage of environmental flow in the drought years considerably. It supplies 69% of water demand in normal years, while the environmental impacts on the river ecosystem are minimized. The proposed model balances the portion of using surface water and ground water in water supply considering environmental impacts on both sources. Using the proposed method is recommendable for optimal environmental management of surface water and ground water in river basin scale.

Effect of Estuary Urbanization on Tidal Dynamics and High Tide Flooding in a Coastal Lagoon

AbstractHigh tide floods (HTFs) are minor, shallow flooding events whose frequency has increased due to relative sea‐level rise (SLR) and secular changes in tides. Here we isolate and examine the role of historical landscape change (geomorphology, land cover) and SLR on tides and HTF frequency in an urbanized lagoonal estuary: Jamaica Bay, New York. The approach involves data archeology, historical (1870s) map digitization, as well as numerical modeling of the bay. Numerical simulations indicate that a century of landscape alterations (e.g., inlet deepening and widening, channel deepening, and wetland reclamation) increased the mean tidal range at the head of the bay by about 20%. The observed historical shift from the attenuation to amplification of semidiurnal tides is primarily associated with reduced tidal damping at the inlet and increased tidal reflection. The 18% decrease in surface area exerts a minor influence. A 1‐year (2020) water level simulation is used to evaluate the effects of both SLR and altered morphology on the annual number of HTFs. Results show that of 15 “minor flood” events in 2020, only one would have occurred without SLR and two without landscape changes since the 1870s. Spectral and transfer function analyses of water level reveal frequency‐dependent fingerprints of landscape change, with a significant decrease in damping for high‐frequency surges and tides (6–18 hr time scale). By contrast, SLR produced only minor effects on frequency‐dependent amplification. Nonetheless, the geomorphic influence on the dynamical response significantly increases the vulnerability of the system to SLR, particularly high‐tide flooding.

Development and implementation of a Direct Surface Description method for free surface flows in OpenFOAM

The solution procedure for two phases in OpenFOAM suffers from unphysical velocity oscillations at the free surface between the two phases. It is likely that this problem also exists in other two-phase Computational Fluid Dynamics (CFD) codes. We aim to solve this by imposing boundary conditions directly on the free surface. We have taken the first step towards a new two-phase solution method by first addressing the water phase alone. It is a free surface modelling method based on merging concepts from two existing methods: (1) A single-phase free surface method and (2) the solution method used in OpenFOAM. The underlying motivation is to enable more accurate estimation of wave induced load distributions from wave crest impacts on offshore structures. This first and foremost requires an accurate prediction of the kinematics near the free surface. We present a solution method with boundary conditions directly on the free surface, thereby the name: Direct Surface Description (DSD). Additionally it is the first time that the isoAdvector algorithm is combined with a single phase free surface method. The implementation is made in OpenFOAM, but may also relevant to other codes as well. First a still water level simulation is presented to illustrate the unphysical behavior of the existing solvers and validate the behaviour of the DSD method. The second test case is a moderately steep stream function wave in intermediate water depth. The DSD method is validated and compared to the existing solution methods of OpenFOAM: interFoam and interIsoFoam . We present a detailed comparison of surface elevations and velocity profiles. This is followed by a convergence study including wave height, velocity and phase shift. Additionally the influence of the Courant–Friedrichs–Lewy (CFL) number is studied. The stream function wave case demonstrates that the DSD method accurately predicts the free surface elevation and velocity fields without free surface undulations or oscillatory velocity fields. The convergence study underlines an increased accuracy of the DSD method. Finally, a 2D and a 3D showcase with breaking waves are presented to show that the DSD method is capable of simulating more complex and realistic cases. • The oscillatory velocities at free surface in OpenFOAM is removed by DSD-method. • Presents a rarely seen comparison of velocity profiles below wave crest and trough. • New DSD-method eliminates the air region leading to decreased CPU time. • Implementation in OpenFOAM makes the method ready for unstructured meshes. • Presents convergence study of velocity and wave heights w.r.t. mesh and CFL number.

Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were {X}_{t-1},; {X}_{t-2}, ;{X}_{t-3}, ;{X}_{t-4}, ; {X}_{t-12}) the ANFIS-WOA model attained root mean square error (RMSE approx 0.08 m), mean absolute error (MAE approx 0.06 m), and coefficient of determination (R2approx 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Using the water balance approach to understand pool dynamics along non-perennial rivers in the semi-arid areas of South Africa

Study regionThe Touws River in the Klein Karoo region of South Africa Study focusThis study sought to improve the understanding of pool dynamics along non-perennial rivers (NPRs) by utilising the water balance approach to assess the water fluxes that influence pool dynamics in the Touws River. The water balance model made use of various in-situ and satellite-derived data. New hydrological insightsThe analysis of the water losses from the pool showed that most of the water was lost through evaporation. The interaction between the pool and groundwater is dependent on the water levels, as the pool loses water to the subsurface up to a certain depth then it starts gaining. When the Wolverfontein 2 pool is full, it can retained water for approximately 258 days without having a surface water inflow. A water balance model was established, and it simulated the water levels with a high correlation of 0.9. This model was also evaluated in the neighbouring pools, and while it simulated the water levels of the upstream pool well, this was not the case for the downstream pool. When remote sensing-derived rainfall and evaporation data were used in the model, the simulated water levels had a slightly lower correlation of 0.7 with the observed water levels. Overall, the remotely sensing-based monthly fluxes estimates could not provide the detailed pool information that was required for the water balance. Errors may have arisen, or they may have been inherited, from any of the three remotely-sensed parameters, namely, the surface area, the rainfall or the evaporation. Although remote sensing did not provide detailed information, it is worth noting that it provides baseline information on the pool dynamics. Overall, this work underscores the relevance of multisource data and the water balance, it helps to better understand the pool dynamics and it will help with the better management of NPRs.

Changes in the salt concentration distribution in surface water (seawater) due to seawater recirculation in a porous medium under tidal fluctuations

Many studies have used numerical simulations and experiments to simulate saltwater intrusion in a nearshore aquifer. Thus, it is known that seawater recirculation occurs in a porous medium just below a sloping beach, and a saltwater wedge occurs in the medium below where the slope intersects the water level at low tide. This study investigated the salt distribution in the surface water when subterranean recirculation of seawater is occurring and there is a saltwater wedge in an underlying porous medium, along with the relationships between the seawater recirculation and the tidal amplitude and beach gradient. The salt distribution in a subterranean aquifer and in surface seawater where the shore sloped gently seaward with a constant slope of 1/10 or 1/5 under tidal amplitudes of 0.5, 1.0, or 2.0 m was numerically simulated by the ASGMF method, which couples water pressure and water flow in a porous medium with those in the overlying surface water and can simulate variable-density flows and the salt concentration distribution in both the porous medium and the surface water. The results showed that the recirculation of seawater depended on the tidal amplitude, being greater when the amplitude exceeded 1.0 m, but that it was unlikely to occur when the beach gradient was steep. Thus, the aspect ratio (width to depth) of the seawater recirculation decreased as the tidal amplitude increased above 1.0 m. Furthermore, the simulated surface water level was often lower than the tidal water level; thus, the surface water level was not consistent with the tidal water level imposed at the right boundary of the simulation domain but varied slightly under the influence of water flow in the surface water and gas flow in the atmosphere. The salt concentration in the surface water was not always the same as the seawater salt concentration because a thin freshwater layer and a brackish water layer of mixed freshwater and saltwater overlay the seawater.

A simple framework for calibrating hydraulic flood inundation models using Crowd-sourced water levels

Floods are the most commonly occurring natural disaster, with the Centre for Research on the Epidemiology of Disasters 2021 report on “The Non-COVID Year in Disasters” estimating economic losses worth over USD 51 million and more than 6000 fatalities in 2020. The hydrodynamic models which are used for flood forecasting need to be evaluated and constrained using observations of water depth and extent. While remotely sensed estimates of these variables have already facilitated model evaluation, citizen sensing is emerging as a popular technique to complement real-time flood observations. However, its value for hydraulic model evaluation has not yet been demonstrated. This paper tests the use of crowd-sourced flood observations to quantitatively assess model performance for the first time. The observation set used for performance assessment consists of 32 distributed high water marks and wrack marks provided by the Clarence Valley Council for the 2013 flood event, whose timings of acquisition were unknown. Assuming that these provide information on the peak flow, maximum simulated water levels were compared at observation locations, to calibrate the channel roughness for the hydraulic model LISFLOOD-FP. For each realization of the model, absolute and relative simulation errors were quantified through the root mean squared error (RMSE) and the mean percentage difference (MPD), respectively. Similar information was extracted from 11 hydrometric gauges along the Clarence River and used to constrain the roughness parameter. The calibrated parameter values were identical for both data types and a mean RMSE value of ∼50 cm for peak flow simulation was obtained across all gauges. Results indicate that integrating uncertain flood observations from crowd-sourcing can indeed generate a useful dataset for hydraulic model calibration in ungauged catchments, despite the lack of associated timing information.

Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning

Vrana Lake on the karst island of Cres (Croatia) is the largest freshwater lake in the Mediterranean islands. The lake cryptodepression, filled with 220 million m3 of fresh drinking water, represents a specific karst phenomenon. To better understand the impact of water level change drivers, the occurrence of meteorological and hydrological droughts was analysed. Basic machine learning methods (ML) such as the multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) were used to simulate water levels. Modelling was carried out considering annual inputs of precipitation, air temperature, and abstraction rate as well as their influential lags which were determined by auto-correlation and cross-correlation techniques. Hydrological droughts have been recorded since 1986, and after 2006 a series of mostly mild hot to moderate hot years was recorded. All three ML models have been trained to recognize extreme conditions in the form of less precipitation, high abstraction rate, and, consequently, low water levels in the testing (predicting) period. The best statistical indicators were achieved with the MNLR model. The methodologies applied in the study were found to be useful tools for the analysis of changes in water levels. Extended monitoring of water balance elements should precede any future increase in the abstraction rate.

Research on Reservoir Operation Rules Extraction Based on Decision Tree and Its Ensemble Model

Reservoir operation rules, as an important tool to guide reservoir operation, are not only the decision-making reference in the period of reservoir planning and design, but also one of the key technologies affecting the comprehensive benefits of the reservoir in the stage of operation and management. Therefore, the reservoir operation rules extraction method based on decision tree and its ensemble model was proposed in this paper. The time index, early water level, inflow, outflow and current inflow were selected to form the input factor set with the reservoir scheduling principle. Comprehensively considering the characteristics of operation data and the principle of decision tree, reservoir water level at the end of period was determined as the model output and then the corresponding simulated water level evaluation index was proposed based on the reservoir regulation capacity. Furthermore, in view of the difficulties in selecting input factors and calibrating the hyperparameters of models, the F test method and mutual information were used as the correlation evaluation index of model input factors, and the Tree-structured Parzen Estimator was introduced. Finally, the proposed methods were applied in Ahai and Ertan Reservoirs, the results showed that the extracted operation rules have high precision, which demonstrated the proposed methods is practicable and effective in the reservoir operation rule extraction.

Backwater Effect of Clogging of Aquatic Plants at Fine-Particle Screens on Inland Flooding in Okayama

In low-lying Okayama city, Japan, the heavy rainfall frequency and intensity have recently increased, subjecting the city to inland flooding. Another factor increasing the inland flooding risk is fine-particle screen clogging by aquatic plants at drainage pump stations, which obstructs drainage and causes backwater. In this study, water level simulations were conducted in drainage pump station channels to clarify the inland flooding risks with and without aquatic plant clogging-induced backwater at fine-particle screens. In the Urayasu West Drainage Pump Station channels, without backwater, no inland flooding occurred under an initial water level of 70% of the channel depth and a 40 m3/s discharge. However, when backwater deeper than 0.2 m occurred under the same conditions, inland flooding occurred, indicating an increased inland flooding risk associated with backwater. Additionally, we conducted an aquatic plant distribution survey in the main Okayama city channels and proposed six priority control sections based on sections with thriving aquatic plants. Although no previous inland flooding studies have considered aquatic plant clogging-induced backwater at fine-particle screens, aquatic plants cause clogging problems and drainage obstructions at water control facilities worldwide. Therefore, this study reveals the importance of conducting water level simulations and distribution surveys in areas other than Okayama city.

The 21st August 2020 Flood in Douala (Cameroon): A Major Urban Flood Investigated with 2D HEC-RAS Modeling

A major flood event occurred on 21 August 2020 in the densely populated Makèpè Missokè neighborhood in the city of Douala (Cameroon, Africa). Nearly 2210 buildings and 12,376 victims spread over 82 hectares were affected. A 2D HEC-RAS model is applied to simulate and characterize this event. A cross analysis of flood depth and flow velocity is used to classify the flood risk and identify areas exposed from low to high hazard. The simulations provide detailed information on the flood characteristics (extent, depth, velocity, arrival time, and duration). The simulated maximum water surface profiles are consistent with the floods marks with differences ranging from 0.02 m to 0.44 m, indicating a good agreement between the observed and simulated water levels at the peak flow (NSE = 0.94, Erel = 0.92, RMSE = 0.21 m). The maximum inundation level is 4.48 m and the flow velocity is globally low at less than 1 m/s. The average flood arrival time and duration are 5 h and 26 h, respectively, for a threshold height of 0.5 m. These results indicate a fast mobilization of the major river channel for the evacuation of this flood. The level of accuracy of the developed model of the 21 August 2020 flood event is appropriate for flood hazard assessment in the city of Douala and is designed to find operational application in future events.

Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping

Flood simulation with two-dimensional hydrodynamic models is subject to different sources of uncertainties in model configuration, boundary conditions, and model parametrization. HEC-RAS 2D is a widely used hydrodynamic model. Here, we assess the sensitivity of the HEC-RAS 2D to its configuration factors and parameters. We evaluate the impacts of different model configuration factors, including the floodplain and channel roughness coefficients, terrain and mesh size, as well as river boundary conditions on the dynamic of water levels, maximum water level, and flood extent, and determine the importance of these contributing factors for reliable flood inundation modeling using both variogram and variance-based analyses. For our case study, we found that depending on the perturbation scale, the performance measure, and the predicted output, the sensitivity to input factors changes. While for simulation of water level dynamics, the DEM and mesh resolutions are the most important factors, for flood extent mapping the floodplain roughness coefficient and the upstream boundary condition are the key factors. In addition, our analyses indicated that for a reliable simulation of maximum flood water level, modelers need to spend resources on the calibration of floodplain roughness coefficient while using fine DEM and mesh resolutions. Here, we also investigate the role of computational time interval and mesh resolution on the model's run time and accuracy. Our results suggested that reducing the computational time interval has minimum impact, as it increases the model run time without much improving the model accuracy.

Pengaruh Pasang Surut Terhadap Profil Muka Air Banjir Bantaran Sungai Musi Kota Palembang

Palembang is famous for the Musi River with a length of 460 km. The area along the river border is vulnerable to the impact of floods, this is the same thing that occurs in the Gandus District, which is mostly located on the banks of the Musi River. Analysis of the flood water level profile along the banks of the Musi River, which is affected by the tides is needed to find out how big the impact of flooding in the study area is. It aims to determine the type of tide, the amount of flow discharge, and simulation of river water level. Primary data was taken using a current meter to determine the speed of river flow. Some of the secondary data needed are river channel topographic data, rainfall data for 10 years, channel cross-section data and Musi River level data. The analysis was carried out in the form of a simulation of flood water level with the help of the HEC-RAS 5.0.7 program. The tidal type of the Musi River is based on the results of the study in the form of Diurnal Tide, with a watershed area of 2.2 km2 in Gandus District, the runoff discharge is 280.34 m3/s. The simulation results with the help of the HEC-RAS program obtained an average runoff height on the Musi River border of 2.7 m. The high runoff from the simulation can have a negative impact on the surrounding community, so there needs to be serious handling for flood control.

Effect of inlet morphodynamics on estuarine circulation and implications for sustainable oyster aquaculture

The incidence of nutrient-induced hypoxia or anoxia-related mortality of aquatic species is becoming more prevalent in coastal systems worldwide. One such instance in an estuary of New Brunswick, Canada, resulted in the loss of approximately 75% of cultured oysters in certain farms. Field observations revealed that, although the occurrence of summer hypoxia in north Tracadie Bay was linked with anthropogenic stressors, changes in the morphology of one of the bay's inlets were likely to play a significant role in escalating the eutrophic conditions. However, adequate information on the circulation dynamics of Tracadie Bay was lacking. To address this knowledge gap, a high-resolution spatially explicit hydrodynamic model was developed for Tracadie Bay to evaluate the possible physical processes involved in the generation of hypoxia leading to oyster mortality. Further, the model was coupled with a volume advection-dispersion tracer module to track the dissemination of effluent. The model showed high skill in south Tracadie Bay in simulating water level elevations and slightly lower skill in north Tracadie Bay. Residual flow estimates revealed poorly-circulated stagnant areas, consistent with hot spots in sediment organic matter content. Findings from modeling scenarios based on past, present, and future inlet morphology suggest that inlets have strong consequences on intricate circulation patterns, renewal of water, and transport. Outcomes from this study will be of relevance for managing water quality and aquaculture practices.

Effects of high nitrogen concentration and low water level on the growth of the submerged macrophyte Vallisneria spinulosa

The problem of seasonal drought has intensified in recent years. Unfortunately, dry seasons can lead to poor water quality due to nitrogen (N) accumulation, which, in turn, may result in the gradual decline or even disappearance of submerged plants. This outdoor study was conducted to determine the morphological, biomass responses and C/N metabolism of Vallisneria spinulosa to the high N content and low water level. We used two N contents (i.e., low and high) and three simulated water levels (i.e., gradual water level increase from 60 cm to 150 cm over four periods (C), low water level of 60 cm maintained for three periods followed by an abrupt increase to 150 cm in the fourth period (CE) and low water levels of 60 ± 30 cm maintained for three periods followed by an abrupt increase to 150 cm in the fourth period (E)). For the low N content, low water levels for CE and E treatments caused increased ramet number and biomass of V. spinulosa. However, for the high N content, the low water levels did not cause such big changes in V. spinulosa. High N contents demonstrated a remarkably negative influence on the biomass of each organ (included leaf, stolon, root and tuber) of V. spinulosa and flowering ramet number. For C water level, the high N content caused an increase in free-amino acid (FAA) content in stolons and leaves, and no difference in soluble carbohydrate (SC) content. For E water level, there was no significant increase in FAA content of V. spinulosa under high N treatment; furthermore, there was no significant change in SC content under high N treatment except for leaves. In conclusion, low water levels exacerbated the damage to V. spinulosa caused by high nitrogen level.

A coupled surface-subsurface flow model for simulating soil-water dynamics in lowland rice field under alternate wetting and drying conditions

The lack of coupled surface-subsurface flow models applied in lowland rice irrigation systems is the prime mover for this research. This study aimed to develop a coupled model that can capture the soil-water dynamics in paddy rice fields and to calibrate and validate the model using actual field data to serve as basis for irrigation water management using alternate wetting and drying (AWD) for lowland rice production systems. The zero-inertia surface flow model was internally coupled with the Richards-based subsurface flow model through Python scripts. Boundary conditions were set accordingly based on lowland rice field scenarios. Irrigation advance times were observed manually, while field water recession data were collected using wireless sensors. Model calibration showed acceptable results for the surface flow (R2≥0.99; SE≥0.97; RMSE≤3.14min), and subsurface flow (R2≥0.71; NSE≥0.60; MSE≤8.78mm; SD≤5.51mm; AE≤7.51mm) domains. Likewise, model validation yielded acceptable results for surface flow (R2≥0.98; NSE≥0.88; RMSE≤6.12min) and subsurface flow (R2≥0.76; NSE≥0.60; RMSE≤10.87mm; SD≤7.91mm; AE≤8.28mm), respectively. Results also showed the model’s capability to simulate water level fluctuations in AWD-irrigated rice fields during the wet and dry seasons under various rainfall scenarios. Model projections showed the need for AWD irrigation practice when the normal rainfall had been reduced by 75% during the wet season. Dry season simulations indicated the need for AWD under all rainfall scenarios. Simulation results have further unveiled the importance of the plow pan characteristics in influencing the soil-water flow behavior. This study was also able to address the limitations of the existing coupled models that could not meet the boundary conditions of paddy rice field settings. Ultimately, the model developed in this study could serve as basis for the development of optimum irrigation operation schemes in AWD -irrigated rice fields.

An estimation of water levels associated with a storm along the coast of Bangladesh using a non-central difference method of lines

In this paper, depth-averaged shallow water equations were solved using a non-central difference method of lines together with the classical RK(4,4) method to estimate water levels associated with a cyclone along the coast of Bangladesh. A 4-point backward finite difference method was adopted in discretizing spatial derivatives retaining time derivatives as continuous. Thus, the shallow water equations together with the boundary conditions were transformed into a system of initial value problems, which was solved using the RK(4,4) method. A nesting approach was employed to incorporate the land-sea interface and bottom topography effectively with economic cost. A stable tidal oscillation was generated in the domain applying the M2 tidal constituent along the southern boundary of the parent model and the surge model was then run to get water levels owing to dynamic tide-surge interaction. Numerical experiments were performed to simulate water levels due to tide, surge, and nonlinear tide-surge interactions associated with the cyclonic storm, AILA. The obtained results were in good agreement with those attained in some previous studies and observations and the WLs due to the nonlinear tide-surge interaction were in a reasonable agreement with respect to the root mean square error values and predictive skills.

An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie

Abstract. For enhanced public safety and water resource management, a three-dimensional operational lake hydrodynamic forecasting system, COASTLINES (Canadian cOASTal and Lake forecastINg modEl System), was developed. The modeling system is built upon the three-dimensional Aquatic Ecosystem Model (AEM3D) model, with predictive simulation capabilities developed and tested for a large lake (i.e., Lake Erie). The open-access workflow derives model forcing, code execution, post-processing, and web-based visualization of the model outputs, including water level elevations and temperatures, in near-real time. COASTLINES also generates 240 h predictions using atmospheric forcing from 15 and 25 km horizontal-resolution operational meteorological products from the Environment Canada Global Deterministic Forecast System (GDPS). Simulated water levels were validated against observations from six gauge stations, with model error increasing with forecast horizon. Satellite images and lake buoys were used to validate forecast lake surface temperature and the water column thermal stratification. The forecast lake surface temperature is as accurate as hindcasts, with a root-mean-square deviation <2 ∘C. COASTLINES predicted storm surges and up-/downwelling events that are important for coastal flooding and drinking water/fishery management, respectively. Model forecasts are available in real time at https://coastlines.engineering.queensu.ca/ (last access: January 2022​​​​​​​). This study provides an example of the successful development of an operational forecasting workflow, entirely driven by open-access data, that may be easily adapted to simulate aquatic systems or to drive other computational models, as required for management and public safety.