Surface Water Quality Modeling Research Articles

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Surface water quality modelling with data scarcity in semi-enclosed coastal regions encompassed distributed islands

Water quality variation in semi-enclosed urban coastal areas with different pollutant sources is a substantial issue. Pollutant entrapment has a significant impact on the lives of the local people. Surface water quality modelling often requires large datasets covering bathymetry, fluid and pollutants boundary conditions, sources and sinks. This is particularly challenging in regions with complex features and poor data availability, such as coastal water bodies featuring a large number of widely distributed islands and estuaries. In this paper, we developed a model of surface water quality and hydrodynamics for Ha Long Bay, in the North of Vietnam and includes 1969 islands, for a one-year period using a water quality dataset obtained for this study. The model utilized extracted bathymetric and geometric data from Admiralty Charts and Admiralty Tide Tables. Water quality coupled with the TELEMAC Model (WAQTEL) Biomass Module was employed to predict pollutant transport in the domain using point and diffused sources while field studies were conducted to collect data for the setting up and calibration of the water quality model. Thirty scattered sampling points were selected, and the water quality parameters were measured during two campaigns. The predicted water level and velocity values matched the local observation data well with a small error (RMSE = 0.19 and 0.16). Both NO3−-N and PO43--P were high near the shoreline and decrease gradually offshore. The maximum concentrations of NO3−-N and PO43--P reached 0.476 mg/L and 0.048 mg/L at the end of 2021 with the RMSE = 0.13 and 0.011, respectively. The levels of NO3−-N and PO43--P and their distributions showed that Ha Long Bay was eutrophic even during the COVID-19 lockdown period.

Assessment of Regression Models for Surface Water Quality Modeling via Remote Sensing of a Water Body in the Mexican Highlands

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.

DynQual v1.0: a high-resolution global surface water quality model

Abstract. Maintaining good surface water quality is crucial to protect ecosystem health and for safeguarding human water use activities. However, our quantitative understanding of surface water quality is mostly predicated upon observations at monitoring stations that are highly limited in space and fragmented across time. Physical models based upon pollutant emissions and subsequent routing through the hydrological network provide opportunities to overcome these shortcomings. To this end, we have developed the dynamical surface water quality model (DynQual) for simulating water temperature (Tw) and concentrations of total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) with a daily time step and at 5 arcmin (∼ 10 km) spatial resolution. Here, we describe the main components of this new global surface water quality model and evaluate model performance against in situ water quality observations. Furthermore, we describe both the spatial patterns and temporal trends in TDS, BOD and FC concentrations for the period 1980–2019, and we also attribute the dominant contributing sectors to surface water pollution. Modelled output indicates that multi-pollutant hotspots are especially prevalent across northern India and eastern China but that surface water quality issues exist across all world regions. Trends towards water quality deterioration have been most profound in the developing world, particularly sub-Saharan Africa and South Asia. The model code is available open source (https://doi.org/10.5281/zenodo.7932317, Jones et al., 2023), and we provide global datasets of simulated hydrology, Tw, TDS, BOD and FC at 5 arcmin resolution with a monthly time step (https://doi.org/10.5281/zenodo.7139222, Jones et al., 2022b). These data have the potential to inform assessments in a broad range of fields, including ecological, human health and water scarcity studies.

Surface Water Quality Modelling

Surface water quality modelling has become an important means of better understanding aquatic and riparian ecosystem processes at all scales, from the micro-scale (e [...]

Surface Water Quality Assessment, Prediction, and Modeling of the River Daya in Odisha

A decision tree-based approach is projected to predict surface water quality and is a good tool to assess the quality and guarantee the safe use of water for drinking. Modeling surface water quality using artificial intelligence-based models is essential in projecting suitable mitigation measures; however, it remains a challenge and requires further research to enhance the modeling accuracy. Because of the serious effects of low water quality, a faster and less expensive solution is required. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality. The objective of this study is to assess the surface water quality of the Daya watercourse to determine the optimal procedure to measure quality of drinking water. Samples were collected from designated locations throughout different seasons (winter, summer, rainy) over a period of five years (2016, 2017, 2018, 2019, and 2020). Total dissolved solids, pH, alkalinity, chloride, nitrate, total hardness, calcium, magnesium, iron, fluoride, were all tested, as well as total coliform, fecal coliform, and E. coli. Through this decision tree regression model, accuracy of prediction is 93.77%. This is a significant result, indicating that the decision tree-based approach has the potential to be a useful tool for surface water quality prediction. However, it is important to note that there may be limitations and uncertainties in the model, and further research and validation may be required to improve the accuracy and dependability of forecasts. The catastrophic consequences of poor water quality, as well as the need for faster and less expensive technologies for testing water quality, are the driving factors in this study. The study's findings can help to improve knowledge of water quality in the Daya watercourse and enhance the decision-making processes to ensure safe drinking water.

Current wastewater treatment targets are insufficient to protect surface water quality

The quality of global water resources is increasingly strained by socio-economic developments and climate change, threatening both human livelihoods and ecosystem health. With inadequately managed wastewater being a key driver of deterioration, Sustainable Development Goal (SDG) 6.3 was established to halve the proportion of untreated wastewater discharged to the environment by 2030. Yet, the impact of achieving SDG6.3 on global ambient water quality is unknown. Addressing this knowledge gap, we develop a high-resolution surface water quality model for salinity as indicated by total dissolved solids, organic pollution as indicated by biological oxygen demand and pathogen pollution as indicated by fecal coliform. Our model includes a novel spatially-explicit approach to incorporate wastewater treatment practices, a key determinant of in-stream pollution. We show that achieving SDG6.3 reduces water pollution, but is still insufficient to improve ambient water quality to below key concentration thresholds in several world regions. Particularly in the developing world, reductions in pollutant loadings are locally effective but transmission of pollution from upstream areas still leads to water quality issues downstream. Our results highlight the need to go beyond the SDG-target for wastewater treatment in order to achieve the overarching goal of clean water for all.

Using sUAS for the Development and Validation of Surface Water Quality Models in Optically Deep Mine Waters

Using sUAS for the Development and Validation of Surface Water Quality Models in Optically Deep Mine Waters

Pathway to encapsulate the surface water quality model and its applications as cloud computing services and integration with EDSS for managing urban water environments

The encapsulation of specific management measurements and computing services is rarely reported for surface water EDSS. To promote the efficiency and reduce the complexity of surface water EDSS development, a generic pathway to encapsulate the surface water quality model and its applications as cloud computing services is proposed. The web service is implemented under the OpenGMS framework. Two typical management measurements, water assimilative capacity allocation and pollution load reduction planning are encapsulated based on the advanced version of the WASP program. An EDSS instance of the Maozhou River, Shenzhen, is developed, which provides precise management measurements for COD and NH3–N control. Management services provided by other models, EFDC and SWMM, are also implemented under this novel framework. The proposed pathway provides the merits of rapid development, easy deployment, and maintenance of an advanced EDSS. It presents a good demonstration of coupling advanced WASP programs within EDSS for receiving water management.

THE ASSESSMENT OF TECK'S CASTLE PROJECT BASED ON FIVE VALUED ECOSYSTEM COMPONENTS

Teck's Castle Project is the largest coal mine project to be mined in Canada. This article is an environmental assessment of Teck's Castle Project based on five valued ecosystem components (VECs) including: Surface Water Quality, Fish and Fish habitat, Vegetation, Local Employment, and Land Use. I proposed to use a surface water quality model to detect the degree of pollution of the water quality of the surrounding rivers and use an economic multiplier to measure the impact on local economic employment. Through research, I found that the water treatment facilities used by Teck Coal Limited can effectively alleviate the impact of the project on the water quality of the surrounding rivers, and I recommended that Teck Coal Limited wear protective equipment to protect their health when working.

The Effects of Climate Change on the Reuse of Agricultural Drainage Water in Irrigation

Climate change is projected to have a major impact on agricultural water resources availability. Comprehending the effects of climate change on water supplies for agriculture is an essential aid for the effective management of available water resources. A forecasting model, a surface water quality model coupled with an optimization algorithm have been used in forecasting agricultural water quality and to maximize the reuse of wastewater in the Osan watershed, South Korea. In forecasting water quality and streamflow parameters for a 30 year period, AutoRegressive Integrated Moving Average (ARIMA) was used. Both ARIMA model fit and model performance were within accepted levels. Water quality and quantity were simulated using the QUAL2Kw model. Jaya algorithm was executed to optimize the process for determining optimal quantities for agricultural drainage water (ADW) reused at withdrawal points. Simulations of water quality show that organic matter levels are key attributes in the management of the Osan drain. QUAL2Kw model calibration and validation show that the model can be applicable with values of Nash—Sutcliffe model efficiency (NSE) > 0.5 respectively. Water quality results show that Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Total Nitrogen (TN) and Total Suspended Solids (TSS) were within acceptable ranges. ADW is suitable for indirect irrigation in the Osan watershed as water quality standards meets Korea’s standards and Food and Agricultural Organization (FAO) Guidelines. Irrigation water demand will increase by 47.1 % for summer and by 16.5% for winter by 2050 due to an increase in temperature. The total agricultural ADW reuse amounts will increase by 47.4% for summer and 17.5% for winter by 2050 due to the creation of farmlands, as there will be an increase in food demand. Optimization results show that drainage water availability will increase by 31.6% and 21.3% respectively for summer and winter by 2050 due to climate change effects. Overall, the results show that the reuse of ADW demand and the available amounts is most likely affected by climate change.

English

Accurate quantification of heavy metals is essential for optimizing remedial efforts at polluted waterfronts. Although surface water quality models can be helpful tools for rapid quantification of heavy metals in streams, they cannot be extrapolated to other water bodies. This study estimated Mg, Cd, and Ni levels in one of the polluted urban waterfronts in the Niger Delta Province, Nigeria (5.317°N, 6.467°E) using one-dimensional transport model. To do this, water samples were collected along the waterfront in both dry and rainy seasons and analyzed for the selected heavy metals using a SHIMADZU® AA6800 Atomic Absorption Spectrophotometer according to international standards. Model parameters were determined by simple linear regression using the experimental dataset. Results showed that the average concentrations of Mg (5.964 mg/L), Cd (0.088 mg/L), and Ni (0.071 mg/L) in the waterfront in both dry and rainy seasons exceeded both local and international regulatory values. The seasonal variation of the heavy metals in the waterfront showed that the concentrations of Mg and Cd were higher during the dry season. Model validation of experimental data showed reasonable prediction precision with root mean square error (RMSE) of 0.0016-0.2254 mg/L in the dry season, RMSE of 0.0026-0.3259 mg/L in the rainy season, and corresponding average validation r2 of 0.985 in both seasons. These results suggest that the waterfront is laden with heavy metals and the one-dimensional transport model is a useful tool to rapidly estimate the levels of Mg, Cd, and Ni in the waterfront for pollution control. Key words: Environmental monitoring, heavy metals, water pollution, water quality modelling. &nbsp

Benchmarking the efficiency of a metamodeling-enabled algorithm for the calibration of surface water quality models

Abstract Computational efficiency is a major obstacle imposed in the automatic calibration of numerical, high-fidelity surface water quality models. To surpass this obstacle, the present work formulated a metamodeling-enabled algorithm for the calibration of surface water quality models and assessed the computational gains from this approach compared to a benchmark alternative (a derivative-free optimization algorithm). A radial basis function was trained over multiple snapshots of the original high-fidelity model to emulate the latter's behavior. This data-driven proxy of the original model was subsequently employed in the automatic calibration of the water quality models of two water reservoirs and, finally, the computational gains over the benchmark alternative were estimated. The benchmark analysis revealed that the metamodeling-enabled optimizer reached a solution with the same quality compared to its benchmark alternative in 20–38% lower process times. Thereby, this work manifests tangible evidence of the potential of metamodeling-enabled strategies and sets out a discussion on how to maximize computational gains deriving from such strategies in surface water quality modeling.

A novel approach for surface water quality modelling based on Landsat-8 tasselled cap transformation

ABSTRACT Updating the physicochemical properties of surface water is important to water quality researchers and decision-makers. Routine surface water quality monitoring methods are inadequate; whereas, remote sensing (RS) is a source of processing data in both spatial and temporal domains. However, RS is a good tool of monitoring freshwater ecosystems, accurate modelling of surface water quality parameters from satellite imagery is still a challenging task. High correlation between the adjacent satellite signals, which creates redundancy, is one of the main challenges that negatively affect the stability of the developed models. Therefore, this paper focuses on the development of a novel approach for modelling concentrations of both turbidity (T) and total suspended solids (TSS) using Landsat-8 tasselled cap transformation (TCT). As a result, robust models were developed between measurements from satellite-derived T and TSS and in situ measurements with coefficient of determination values of 0.854 and 0.812, respectively. These findings confirmed the possibility of using TCT to establish strong models for assessing surface water quality due to its capability of transforming a set of correlated data onto a convenient set of uncorrelated data, which maintain the major information associated with a physical phenomenon.

Sensitivity Analysis of Input Data in Surface Water Quality Models

Abstract The article is focused on analysis of input data impact on outputs of water quality models. The authors examined the impact of roughness coefficient, both boundary and initial conditions setup on changes of outputs generated by HEC-RAS model. Simulation results have shown a various response rate of input data on simulated results. The strong impact shows roughness coefficient setup that through the value of longitudinal dispersion coefficient affects pollution transport process. Changes in boundary conditions have had less influence on outputs. Relatively strong impact shows the setup of initial state of pollution concentration along the reach mainly for the case of low gradient rivers.

Modeling of Water Quality Evolution with Advanced Hydroinformatic Tool. Study Case: Bega Channel Sector

Water quality evolution modeling is very important to predict the changes in surface water quality for water resources and environmental management in the world. Worldwide, in the last decades, hundreds of surface water quality models have been developed. Water quality models based on advanced hydroinformatic tools are an efficient way to simulate and predict the pollutant transport in water courses, lakes and reservoirs, which can contribute to saving the cost of labors and materials for a large number of sampling and chemical experiments to determine the degree of pollution of water bodies. Surface water quality models are useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body, in different scenarios of pollution (point source, distributed source and accidentally pollution). The modeling results from these models are very important components of environmental impact assessment and can provide a basis and a technique support for specialists from the water resources and environmental management authorities to make the right decisions. For the case study, we use MIKE11 advanced hydroinformatic tool. MIKE11, part of the DHI software products, is a professional engineering software package for the simulation of flows, water quality and sediment transport in estuaries, rivers, irrigation systems, channels and other water bodies. MIKE11 is a user-friendly, fully dynamic, one-dimensional modeling tool for the detailed analysis, design, management and operation of both simple and complex river and channel systems. The used modules for modeling are Hydrodynamic Module and ECOLab module. The case study was realized on Bega channel sector (city of Timisoara – state border with Serbia), Timis county, Romania.

Modelling the surface water quality of the Agrioune River in the north-east of Algeria by the PEGASE model

ABSTRACT In this study, the physicochemical parameters of the river streams of the Agrioune watershed were evaluated by experimental monitoring and surface water quality modelling. The PegOpera Software suite was used to apply the PEGASE model to the Agrioune River/Basin (north-east of Algeria). This model was used to simulate different parameters concentrations, its application on the Agrioune watershed case is one of the first works that are currently being carried out on the semi-arid regions. In addition to low flows, the surface river water quality in these regions is mostly influenced by urban discharges and agricultural activities. Seven parameters are predicted based on the existing distribution of point and diffuse pollution sources, they are compared to available measured values. A significant correlation was obtained between the simulated and the measured values after calibration of some model inputs. Model performance showed good statistical agreements with a Nash-Sutcliffe efficiency (NSe) oscillating between 0.44 and 0.89 and with determination coefficient (R 2) varying between 0.66 and 0.94 for the whole studied parameters. The simulation allowed to locate and to distinguish five different river water classes according to Seq-Eau model. Theses quality classes pointed out some river zones with unsatisfactory physicochemical status, and that have to be addressed with priority measures in order to improve their quality. Furthermore, the model was also used to simulate the impact of replacing settling basins by WWTP in the lower part of the Agrioune watershed. The results showed satisfactory reduction of some pollution parameters concentrations. The PEGASE model and the present study may be useful and may constitute a practical tool for managers to simulate several scenarios for improving the water quality in the Agrioune watershed.

Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia.

A river water quality spatial profile has a diverse pattern of variation over different climatic regions. To comprehend this phenomenon, our study evaluated the spatial scale variation of the Water Quality Index (WQI). The study was carried out over four main climatic classes in Asia based on the Koppen-Geiger climate classification system: tropical, temperate, cold, and arid. The one-dimensional surface water quality model, QUAL2Kw was selected and compared for water quality simulations. Calibration and validation were separately performed for the model predictions over different climate classes. The accuracy of the water quality model was assessed using different statistical analyses. The spatial profile of WQI was calculated using model predictions based on dissolved oxygen (DO), biological oxygen demand (BOD), nitrate (NO3), and pH. The results showed that there is a smaller longitudinal variation of WQI in the cold climatic regions than other regions, which does not change the status of WQI. Streams from arid, temperate, and tropical climatic regions show a decreasing trend of DO with respect to the longitudinal profiles of main river flows. Since this study found that each climate zone has the different impact on DO dynamics such as reaeration rate, reoxygenation, and oxygen solubility. The outcomes obtained in this study are expected to provide the impetus for developing a strategy for the viable improvement of the water environment.

A stochastic approach for regional-scale surface water quality modeling

A methodology is proposed to calculate statistical average and standard deviation of long time water quality parameter series along a river network. The method considers the water network as a graph consisting of straight sessions and junctions. With a Taylor-series approximation, statistical values of an arbitrary point of the network can be calculated from upstream ones without the need to calculate the single downstream values. According to preliminary results of the first calculations on a pilot area, mean value of the downstream biological oxygen demand and the so called ‘transfer coefficient’ can be approximated with a relative accuracy of 10%.

Parameter Sensitivity of a Surface Water Quality Model of the Lower South Saskatchewan River—Comparison Between Ice-On and Ice-Off Periods

Little is known about seasonal differences (ice-on vs. ice-off periods) and the sensitivity of in-stream processes to surface water quality constituents in rivers that have a persistent ice cover in winter. The goal of this study is to investigate the sensitivity of nutrient transformation processes on surface water quality, especially rivers in cold regions where ice-covered conditions persist for a substantial part of the year. We established a sensitivity analysis framework for water quality modelling and monitoring of rivers in cold regions using the Water Quality Analysis Program WASP7. The lower South Saskatchewan River in the interior of western Canada, from the Gardiner Dam at Lake Diefenbaker to the confluence of the North and South Saskatchewan rivers, is used as a test case for this purpose. The study reveals that parameter sensitivities differ between ice-covered and ice-free periods and biological model parameters related to nutrient-phytoplankton dynamics can still be sensitive during the ice-covered season. For example, sediment oxygen demand is an important parameter during the ice-on period, whereas parameters related to nitrification are more sensitive in the ice-off period. These results provide insight into important water quality monitoring aspects in cold regions during different seasons.