Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

NATIONWIDE REGRESSION MODELS FOR PREDICTING URBAN RUNOFF WATER QUALITY AT UNMONITORED SITES1

ABSTRACT: Regression models are presented that can be used to estimate mean loads for chemical oxygen demand, suspended solids, dissolved solids, total nitrogen, total ammonia plus nitrogen, total phosphorous, dissolved phosphorous, total copper, total lead, and total zinc at unmonitored sites in urban areas. Explanatory variables include drainage area, imperviousness of drainage basin to infiltration, mean annual rainfall, a land-use indicator variable, and mean minimum January temperature. Model parameters are estimated by a generalized-least-squares regression method that accounts for cross correlation and differences in reliability of sample estimates between sites. The regression models account for 20 to 65 percent of the total variation in observed loads.     

GEOHYDROLOGY AND HYDROCHEMISTRY OF THE DAKOTA AQUIFER,CENTRAL UNITED STATES1

ABSTRACT: The Dakota aquifer, composed of the Dakota Sandstone and stratigraphically equivalent sandstone units of Cretaceous age, is the upper-most regional aquifer underlying the extensively developed High Plains aquifer of the midwestern United States. The concentration of dissolved solids in ground water of the Dakota aquifer ranges from less than 500 milligrams per liter in calcium bicarbonate type water in the eastern outcrop area to more than 100,000 milligrams per liter in sodium chloride type oilfield brine in the Denver Basin to the west. Preliminary maps showing the distribution of dissolved solids confirm the complex nature of the Dakota aquifer as inferred from stratigraphic and hydraulic evidence. Extensive vertical leakage through confining layers, local recharge at the truncated eastern boundary, and a barrier to recharge along the western edge of the Denver Basin are consistent with the distribution of hydraulic head and dissolved solids.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

University of Benin water supply system: Microbiological and physico-chemical assessments

The microbiological and physico-chemical characteristics of the drinking water supplied by the Central Borehole at the University of Benin, Ugbowo Campus were investigated. The investigation entailed assessment of the pH, turbidity, total suspended solids, total dissolved solids, dissolved oxygen, temperature, salinity, conductivity, nitrate, nitrite, phosphate, sulphate, chloride, N-nitroso compounds, cadmium, chromium, nickel, lead, zinc, manganese, iron, coliform count, BOD₅ and COD of the water at the Central Borehole and at ten residential quarters. The assessment indicated that the water was fit for drinking and other domestic applications. Results were also compared with WHO, EU and Nigeria FEPA standards. The results showed that the pH values of the water (5.01–5.86) and total coliform count (1–2/100 ml) expressed as MPN were outside the limits set by the WHO, EU and FEPA. The data also showed that the other water quality parameters assessed were within WHO, EU and FEPA permissible limits. The results of ANOVA showed that significant changes occurred during distribution.     

THE INFLUENCE OF ENGINEERING DESIGN AND OPERATION AND OTHER ENVIRONMENTAL FACTORS ON RESERVOIR FISHERY RESOURCES1

The apparent effect of selected reservoir environmental variables-including surface area, mean depth, outlet depth, thermocline depth, water level fluctuation, storage ratio, shore development, total dissolved solids, growing season and age of reservoir–on fish standing crop in 140 large impoundments has been explored through partial correlation and multiple regression analyses. The sample was partitioned into 25 subsamples based on reservoir use type, water exchange rate, thermocline formation and water chemistry. Fish standing crops were estimated by summer rotenone sampling of coves or open water areas enclosed by blockoff net. Logarithmic partial correlation revealed highly significant (0.01 confidence shore development and dissolved solids on At the 0.20 confidence level, the crop of storage ratio and shore level) positive effects of outlet depth, total standing crop in the entire sample. all sport fishes is positively influenced by outlet depth, development and negatively by mean depth. In 54 hydropower reservoirs with a stable thermocline, positive effects of increased storage ratio and dissolved solids on t o t a l crop are evident at the 0.05 confidence interva. Increase in thermocline depth has a negative effect. In 25 hydropower reservoirs without a stable thermocline, clupeid (shad) crop is negatively correlated with surface area, mean depth and fluctuation. Reservoirs with a thermocline have higher standing crops than those without. At the species or species group level, partial correlation of nine environmental variables a t the 0.05 confidence interval reveals: Positive effect of surface area on pike and pickerel; buffalo-fishes, white crappie and total sport fish crop; positive effect of outlet depth on largemouth bass, catfishes, total sport fish crop and buffalofishes; a negative effect of water level fluctuation on pike and pickerel, redear sunfish and gizzard shad; a positive relationship between storage ratio and channel catfish and bull- heads and a negative one with flathead catfish and suckers; a positive effect of total dissolved solids on black and whit basses, catfishes, gizzard shad, carpsuckers and carp. A morphoedaphic expression, total dissolved solids divided by mean depth, provides a useful index t o reservoir fish production. The relationship is curvilinear, with maximum crops expected at index values of 5 t o 30.The index accounts for 62 percent of the variability in hydropower storage reservoir crops. Several multivariable regressions have been derived f o r predictive purposes. Examples are included, with R values of 35 t o 60.     

Enhancing perspectives on lake impairments using satellite observations: A case study on High Rock Lake,North Carolina

Stakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in-situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state-wide nutrient management planning processes. An existing in-situ database was used to calibrate semi-analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat-7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in-situ match-ups for model calibration and validation. Model r-squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in-situ data and simulated TSS values produced by a calibrated hydrologic-hydrodynamic model. The process-based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy.     

Modeled Sources,Transport, and Accumulation of Dissolved Solids in Water Resources of the Southwestern United States1

Anning, David W., 2011. Modeled Sources, Transport, and Accumulation of Dissolved Solids in Water Resources of the Southwestern United States. Journal of the American Water Resources Association (JAWRA) 47(5):1087‐1109. DOI: 10.1111/j.1752‐1688.2011.00579.x Abstract: Information on important source areas for dissolved solids in streams of the southwestern United States, the relative share of deliveries of dissolved solids to streams from natural and human sources, and the potential for salt accumulation in soil or groundwater was developed using a SPAtially Referenced Regressions On Watershed attributes model. Predicted area‐normalized reach‐catchment delivery rates of dissolved solids to streams ranged from <10 (kg/year)/km² for catchments with little or no natural or human‐related solute sources in them to 563,000 (kg/year)/km² for catchments that were almost entirely cultivated land. For the region as a whole, geologic units contributed 44% of the dissolved‐solids deliveries to streams and the remaining 56% of the deliveries came from the release of solutes through irrigation of cultivated and pasture lands, which comprise only 2.5% of the land area. Dissolved‐solids accumulation is manifested as precipitated salts in the soil or underlying sediments, and (or) dissolved salts in soil‐pore or sediment‐pore water, or groundwater, and therefore represents a potential for aquifer contamination. Accumulation rates were <10,000 (kg/year)/km² for many hydrologic accounting units (large river basins), but were more than 40,000 (kg/year)/km² for the Middle Gila, Lower Gila‐Agua Fria, Lower Gila, Lower Bear, Great Salt Lake accounting units, and 247,000 (kg/year)/km² for the Salton Sea accounting unit.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

A SEDIMENT RESUSPENSION AND WATER QUALITY MODEL OF LAKE OKEECHOBEE1

ABSTRACT: The influence of sediment resuspension on the water quality of shallow lakes is well documented. However, a search of the literature reveals no deterministic mass-balance eutrophication models that explicitly include resuspension. We modified the Lake Okeechobee water quality model - which uses the Water Analysis Simulation Package (WASP) to simulate algal dynamics and phosphorus, nitrogen, and oxygen cycles - to include inorganic suspend. ed solids and algorithms that: (1) define changes in depth with changes in volume; (2) compute sediment resuspension based on bottom shear stress; (3) compute partition coefficients for ammonia and ortho-phosphorus to solids; and (4) relate light attenuation to solids concentrations. The model calibration and validation were successful with the exception of dissolved inorganic nitrogen species which did not correspond well to observed data in the validation phase. This could be attributed to an inaccurate formulation of algal nitrogen preference and/or the absence of nitrogen fixation in the model. The model correctly predicted that the lake is light-limited from resuspended solids, and algae are primarily nitrogen limited. The model simulation suggested that biological fluxes greatly exceed external loads of dissolved nutrients; and sediment-water interactions of organic nitrogen and phosphorus far exceed external loads. A sensitivity analysis demonstrated that parameters affecting resuspension, settling, sediment nutrient and solids concentrations, mineralization, algal productivity, and algal stoichiometry are factors requiring further study to improve our understanding of the Lake Okeechobee ecosystem.     

IDENTIFICATION OF DISSOLVED-CONSTITUENT SOURCES IN MINE-SITE GROUND WATER USING BATCH MIXING1

ABSTRACT: Batch-mixing experiments were used to help identify lithologic and mineralogic sources of increased concentrations of dissolved solids in water affected by surface coal mining in northwestern Colorado. Ten overburden core samples were analyzed for mineral composition and mixed with distilled water for 90 days until mineral-water equilibrium was reached. Between one day and 90 days after initial contact, specific conductance in the sample mixtures had a median increase of 306 percent. Dissolved-solids concentrations ranged from 200 to 8,700 mg/L in water samples extracted from the mixtures after 90 days. Mass. balance simulations were conducted using the geochemical models BALANCE and WATEQF to quantify mineral-water interactions occurring in five selected sample mixtures and in water collected from a spring at a reclaimed mine site. The spring water is affected by mineral-water interactions occurring in all of the lithologic units comprising the overburden. Results of the simulations indicate that oxidation of pyrite, dissolution of dolomite, gypsum, and epsomite, and cation-exchange reactions are the primary mineral-water interactions occurring in the overburden. Three lithologic units in the overburden (a coal, a sandstone, and a shale) probably contribute most of the dissolved solids to the spring water. Water sample extracts from mixtures using core from these three units accounted for 85 percent of the total dissolved solids in the 10 sample extracts. Other lithologic units in the overburden probably contribute smaller quantities of dissolved solids to the spring water.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

Drying of salted silver jewfish in a hybrid solar drying system and under open sun: Modeling and performance analyses

This study investigated the thin-layer drying kinetics of salted silver jewfish in a hybrid solar drying system and under open sun. Ten drying models were compared with experimental data of salted silver jewfish drying. A new model was introduced, which is an offset linear logarithmic (offset modified Page model). The fit quality of the models was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and sum of squared absolute error (SSAE). The result showed that Midilli et al. model and new model were comparable with two or three-term exponential drying models. This study also analyzed energy and exergy during solar drying of salted silver jewfish. Energy analysis throughout the solar drying process was estimated on the basis of the first law of thermodynamics, whereas exergy analysis during solar drying was determined on the basis of the second law of thermodynamics. At an average solar radiation of 540 W/m² and a mass flow rate of 0.0778 kg/sec, the collector efficiency and drying system efficiency were about 41% and 23%, respectively. Specific energy consumption was 2.92 kWh/kg. Moreover, the exergy efficiency during solar drying process ranged from 17% to 44%, with an average value of 31%. The values of improvement potential varied between 106 and 436 W, with an average of 236 W.     

Limitations for brine acidification due to SO2 co-injection in geologic carbon sequestration

Co-injection of sulfur dioxide during geologic carbon sequestration can cause enhanced brine acidification. The magnitude and timescale of this acidification will depend, in part, on the reactions that control acid production and on the extent and rate of SO₂ dissolution from the injected CO₂ phase. Here, brine pH changes were predicted for three possible SO₂ reactions: hydrolysis, oxidation, or disproportionation. Also, three different model scenarios were considered, including models that account for diffusion-limited release of SO₂ from the CO₂ phase. In order to predict the most extreme acidification potential, mineral buffering reactions were not modeled. Predictions were compared to the case of CO₂ alone which would cause a brine pH of 4.6 under typical pressure, temperature, and alkalinity conditions in an injection formation. In the unrealistic model scenario of SO₂ phase equilibrium between the CO₂ and brine phases, co-injection of 1% SO₂ is predicted to lead to a pH close to 1 with SO₂ oxidation or disproportionation, and close to 2 with SO₂ hydrolysis. For a scenario in which SO₂ dissolution is diffusion-limited and SO₂ is uniformly distributed in a slowly advecting brine phase, SO₂ oxidation would lead to pH values near 2.5 but not until almost 400 years after injection. In this scenario, SO₂ hydrolysis would lead to pH values only slightly less than those due to CO₂ alone. When SO₂ transport is limited by diffusion in both phases, enhanced brine acidification occurs in a zone extending only 5 m proximal to the CO₂ plume, and the effect is even less if the only possible reaction is SO₂ hydrolysis. In conclusion, the extent to which co-injected SO₂ can impact brine acidity is limited by diffusion-limited dissolution from the CO₂ phase, and may also be limited by the availability of oxidants to produce sulfuric acid.     

ESTIMATING REFERENCE EVAPORATION IN THE CENTRAL VALLEY OF CALIFORNIA USING THE LINACRE MODEL1

ABSTRACT: The Linacre (1988) model for calculating evaporation from open water or well-watered surfaces only requires inputs of air temperature, latitude and elevation, and windspeed if it is available. The model was developed using data collected at a large number of sites in different climatic regions of the world, while independent tests of the model have shown it to be suitable for estimating evaporation in a variety of locations. This study was intended to contribute to the broad goal of evaluating temperature-based evaporation models for use in California by testing the Linacre model in the agriculturally intensive Central Valley. Observed monthly mean reference evaporation (E_o) and meteorological data for periods ranging up to 72 months were obtained from 25 California Irrigation and Management Information System (CIMIS) stations distributed throughout the Central Valley. Uncalibrated and calibrated Linacre models were used to estimate monthly mean reference evaporation, and the performance of each model was evaluated using indices that quantified the random and systematic errors and overall model performance. The accuracy of the radiation and ventilation components of the model were evaluated separately. The uncalibrated model was found to systematically overestimate E_o with most of the model error being attributed to the ventilation component. Calibration of the radiation and ventilation components removed most of the systematic model errors, and the root mean square error for monthly mean E_o was 0.676 mm day^?1 (16.8 percent of the mean observed value). (KEY TERMS: reference evaporation; Linacre model; irrigation scheduling.)     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Modeling Water Quantity and Sulfate Concentrations in the Devils Lake Watershed Using Coupled SWAT and CE‐QUAL‐W2

Devils Lake is an endorheic lake in the Red River of the North basin in northeastern North Dakota. During the last two decades, the lake water level has risen by nearly 10 m, causing floods that have cost more than 1 billion USD in mitigation measures. Another increase of approximately 1.5 m in the lake water level would cause spillage into the Sheyenne River. To alleviate this potentially catastrophic spillage, two artificial outlets were constructed. However, the artificial drainage of water into the Sheyenne River raises water quality concerns because the Devils Lake water contains significantly higher concentrations of dissolved solids, particularly sulfate. In this study, the Soil and Water Assessment Tool (SWAT) was coupled with the CE‐QUAL‐W2 model to simulate both water balance and sulfate concentrations in the lake. The SWAT model performed well in simulating daily flow in tributaries with E_NS > 0.5 and |PBIAS| < 25%, and reproduced the lake water level with a root mean square error of 0.35 m for the study period from 1995 to 2014. The water temperature and sulfate concentrations simulated by CE‐QUAL‐W2 for the lake are in general agreement with the field observations. The model results show that the operation of the two outlets since August 2005 has lowered the lake level by 0.70 m. Furthermore, the models show pumping water from the two outlets raises sulfate concentrations in the Sheyenne River from ~100 to >500 mg/L. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.