Effective microorganism water treatment method for rapid eutrophic reservoir restoration.

Since reservoirs perform many important functions, they are exposed to various types of unfavorable phenomena, e.g., eutrophication which leads to a rapid growth of algae (blooms) that degrade water quality. One of the solutions to combat phytoplankton blooms are effective microorganisms (EM). The study aims to evaluate the potential of EM in improving the water quality of the Turawa reservoir on the Mala Panew River in Poland. It is one of the first studies providing insights into the effectiveness of using EM in the bioremediation of water in a eutrophic reservoir. Samples for the study were collected in 2019-2021. The analysis showed that EM could be one of the most effective methods for cleaning water from unfavorable microorganisms (HBN22, HBN36, CBN, FCBN, FEN) - after the application of EM, a reduction in their concentration was observed (from 46.44 to 58.38% on average). The duration of their effect ranged from 17.6 to 34.1 days. The application of EM improved the trophic status of the Turawa reservoir, expressed by the Carlson index, by 7.78%. As shown in the literature review, the use of other methods of water purification (e.g., constructed wetlands, floating beds, or intermittent aeration) leads to an increase in the effectiveness and a prolongation of the duration of the EM action. The findings of the study might serve as a guide for the restoration of eutrophic reservoirs by supporting sustainable management of water resources. Nevertheless, further research should be conducted on the effectiveness of EM and their application in the remediation of eutrophic water reservoirs.

Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation.

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017-2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h-1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash-Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h-1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm-3 just after irrigation in 2 L h-1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm-3 were in 4 L h-1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm-3 were in 8 L h-1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

Eco-hydrological modeling of soil wetting pattern dimensions under drip irrigation systems.

Reliable information on the horizontal and vertical dimensions of the wetted soil beneath a point source is critical for designing accurate, cost-effective, and efficient surface and subsurface drip irrigation systems. Several factors, including soil properties, initial soil conditions, dripper flow rate, number of drippers, spacing between drippers, irrigation management, plant root characteristics, and evapotranspiration, influence the dimensions and shape of wetting patterns. The objective of this study was to briefly review previous studies, collect the analytical, numerical, and empirical models developed, and evaluate the effectiveness of the most common empirical method for predicting the dimensions of soil wetted around drippers using measured data from field surveys. With this review study, we aim to promote a better understanding of soil water dynamics under point-source drip irrigation systems, help improve soil water dynamics under point-source drip irrigation systems, and identify issues that should be better addressed in future modeling efforts. A drip irrigation system was configured with three different emitters with different capacities (2, 4, and 8 l h-1) in the point source to determine the soil wetting front under the point source. The five most selected empirical equations (Al-Ogaidi, Malek and Peters, Amin and Ekhmaj, Li and Schwartzman and Zur) were statistically analyzed to test the efficiency in sandy loam soil. According to the results of the field investigation, statistical comparisons of the empirical models with the field investigation data were performed using the mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe model efficiency (CE), and coefficients of determination (R2). The advanced simulation of the wetting front was used based on the best accuracy of the selected empirical model. In general, the Li model (MAE, RMSE, EF, and R2 were 0.698 cm, 0.894 cm, 0.970 cm2 cm-2, and 0.970, respectively, for the wetted soil width and 1.800 cm, 1.974 cm, 0.927 cm2 cm-2, and 0.986, for the vertical advance) proved to be the best after statistical analysis with field data.

Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions.

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test.

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = -0.010 and RE = -0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = -0.089 and RE = -0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.

A residue management machine for chopping paddy residues in combine harvested paddy field.

Nowadays, Combine Harvesters are the most commonly used device for harvesting crops; as a result, a large amount of plant material and crop residue is concentrated into a narrow band of plant material that exits the combine, challenging the residue management task. This paper aims to develop a crop residue management machine that can chop paddy residues and mix them with the soil of the combined harvested paddy field. For this purpose, two important units are attached to the developed machine: the chopping and incorporation units. The tractor operates this machine as the main source, with a power range of about 55.95 kW. The four independent parameters selected for the study were rotary speed (R1 = 900 & R2 = 1100 rpm), forward speed (F1 = 2.1 & F2 = 3.0 Kmph), horizontal adjustment (H1 = 550 & H2 = 650 mm), and vertical adjustment (V1 = 100 & V2 = 200 mm) between the straw chopper shaft and rotavator shaft and its effect was found on incorporation efficiency, shredding efficiency, and trash size reduction of chopped paddy residues. The incorporation of residue and shredding efficiency was highest at V1H2F1R2 (95.31%) and V1H2F1R2 (61.92%) arrangements. The trash reduction of chopped paddy residue was recorded maximum at V1H2F2R2 (40.58%). Therefore, this study concludes that the developed residue management machine with some modifications in power transmission can be suggested to the farmers to overcome the paddy residue issue in combined harvested paddy fields.

Assessment of future water demand and supply using WEAP model in Dhasan River Basin, Madhya Pradesh, India.

Understanding the available resources and the needs of those who use them is necessary for the evaluation and allocation of water resources. The main sectors utilizing the basin water resources are agriculture, drinking water, animal husbandry, and industries, and the efficient and rational management of water resources to be distributed among those different sectors of activity is vital. This study attempts to develop an integrated water resource management system for the Dhasan River Basin (DRB) by employing a scenario analysis approach in conjunction with Water Evaluation and Planning Model (WEAP) to analyze trends in water use and anticipated demand between 2015 and 2050, simulating five possible scenarios (I, II, III, IV, and V) as for external driving factors. For the WEAP modeling framework, 2015 was chosen as a current (base) year for which all available information and input data were given to the model and the future demand situation was analyzed for the period 2016-2050 (forecasting period). From the findings, it was observed that for the forecasting period, total water demand, unmet demand, and streamflow were 185.29 Bm3, 117.35 Bm3, and 58.26 Bm3, respectively, in the case of scenario I; 232.34 Bm3, 162.17 Bm3, and 59.87 Bm3 in case of scenario II; 139.40 Bm3, 84.37 Bm3, and 58.15 Bm3 in case of scenario III; 186.15 Bm3, 118.76 Bm3, and 56.98 Bm3 in case of scenario IV; and 181.89 Bm3, 96.87 Bm3, and 53.11 Bm3 in case of scenario V. Results of the study indicated that by 2050, increasing population growth, industrial development, and an increase in the agricultural area will rise the water demand dramatically, posing threats to the environment and humans. Therefore, implementing improved irrigation technologies, advancing agricultural practices on farms, and constructing water conservation and retaining structures could significantly reduce the unmet demands and shortfalls in DRB. Overall findings reveal that the pressure on the Dhasan water resources would increase in the future, and thus several suggestions have been provided to assist decision-makers in sustainable planning and management of water resources to meet future demands.

Detection of Water Spread Area Changes in Eutrophic Lake Using Landsat Data.

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann-Kendall test was used. San's slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake's water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions.

Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects.

In recent years, agricultural non-point source pollution (ANPSP) has become the biggest threat to Aras River water quality by completing the Mughan irrigation and drainage network. Nutrient pollutants, including nitrate and phosphate, released into the river through drains have created a range of obstacles for locals living around the river. Agricultural activities are generally considered the largest source of non-point pollution. They have no complex and uniform impact along the river. Thus, the spatial distribution of ANPS and highly polluted areas should be identified to manage watershed management. This study proposes a simple framework for identifying pollutant-sensitive areas along the river and management strategies to improve water quality. To this aim, the main factors affecting ANPSP were identified, and the effectiveness of the scenarios selected to comply with water quality regulations for drinking and environment during 1993-2007 were simulated. Based on the sensitivity analysis, land use and fertilizer are the main factors affecting river ANPSP. Thus, their changes were modeled in different scenarios. Based on the results, the ANPSP load was higher downstream. The agricultural lands in region 3 were considered the main source of pollution. Comparing the management scenarios showed that the amount of nitrate and phosphate leaching into the river decreased to 18.1 and 8.35 %, respectively, by reducing the consumption of urea and phosphate fertilizers by 50 %. The results help watershed managers implement eco-friendly land use and nutrient management programs at specific locations during specific periods to control ANPSP along the rivers.

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models.

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water's temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior (R2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions.

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning's prediction accuracy through optimal weight tuning and evaluate the proposed model's capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R2 of 0.99. The model inputs were also reduced from six variables to only two for cost-effective predictions of daily RET. This shows that the PRSFGWO algorithm is a good RET prediction model for the semi-arid climate region in southern Spain. The results obtained from this research are very promising compared with existing models in the literature.

Cost-effective management measures for coastal aquifers affected by saltwater intrusion and climate change.

Sustainable management of natural water resources and food security in the face of changing climate conditions is critical to the livelihood of coastal communities. Increasing inundation and saltwater intrusion (SWI) will likely adversely affect agricultural production and the associated beach access for tourism. This study uses an integrated surface-ground water model to introduce a new approach for retardation of SWI that consists of placing aquifer fill materials along the existing shoreline using Coastal Land Reclamation (CLR). The modeling results suggest that the artificial aquifer materials could be designed to decrease SWI by increasing the infiltration area of coastal precipitation, collecting runoffs from the catchment area, and applying treated wastewater or desalinated brackish water-using coastal wave energy to reduce water treatment costs. The SEAWAT model was applied to verify that it correctly addressed Henry's problem and then applied to the Biscayne aquifer, Florida, USA. In this study, to better inform Coastal Aquifer Management (CAM), we developed four modeling scenarios, namely, Physical Surface Barriers (PSB), including the artificial aquifer widths, permeability, and side slopes and recharge. In the base case scenario without artificial aquifer placement, results show that seawater levels would increase aquifer salinity and displace large amounts of presently available fresh groundwater. More specifically, for the Biscayne aquifer, approximately 0.50% of available fresh groundwater will be lost (that is, 41,192 m3) per km of the width of the aquifer considering the increasing seawater level. Furthermore, the results suggest that placing the PSB aquifer with a smaller permeability of <100 m per day at a width of approximately 615 m increases the available fresh groundwater by approximately 45.20 and 43.90% per km of shoreline, respectively. Similarly, decreasing the slope on the aquifer-ocean side and increasing the aquifer recharge will increase freshwater availability by about 43.90 and 44.50% per km of the aquifer. Finally, placing an aquifer fill along the shallow shoreline increases net revenues to the coastal community through increased agricultural production and possibly tourism that offset fill placement and water treatment costs. This study is useful for integrated management of coastal zones by delaying aquifer salinity, protecting fresh groundwater bodies, increasing agricultural lands, supporting surface water supplies by harvesting rainfall and flash flooding, and desalinating saline water using wave energy. Also, the feasibility of freshwater storage and costs for CAM is achieved in this study.

Environmental rethinking of wastewater drains to manage environmental pollution and alleviate water scarcity.

The conservation of water resources in developed countries has become an increasing concern. In integrated water resource management, water quality indicators are critical. The low groundwater quality quantitates mainly attributed to the absence of protection systems for polluted streams that collect and recycle the untreated wastewater. Egypt has a limited river network; thus, the supply of water resources remains inadequate to satisfy domestic demand. In this regard, high-quality groundwater is one of the main strategies for saving water supplies with water shortage problems. This paper investigates the critical issues of groundwater protection and environmental management of polluted streams, leading to overcoming water demand-about 18 × 103 km of polluted open streams with a discharge of 9.70 billion Cubic Metter (BCM). We have proposed proposals and policies for the safe use of groundwater and reuse of wastewater recycling for agriculture and other purposes. This study was carried out using the numerical model MODFLOW and MT3DMS-(Mass Transport 3-Dimension Multi-Species) to assess the Wastewater Treated Plant's (WWTP) best location and the critical path for using different lining materials of polluted streams to avoid groundwater contamination. The three contaminants are BOD, COD, and TDS. Five scenarios were applied for mitigating the impact of polluted water: (1) abstraction forcing, (2) installing the WWTP at the outlet of the main basin drain with and without a lining of main and sub-basin streams (base case), (3) lining of main and sub-main streams, (4) installing WWTP at the outlet of the sub-basin streams, and (5) lining of the sub-basin and installing WWTP at the outlet of the sub-basin. The results showed that the best location of WWTP in polluted streams is developed at the outlets of sub-basin with the treatment of main basin water and the lining of sub-basins streams. The contamination was reduced by 76.07, 76.38, and 75.67% for BOD, COD, and TDS, respectively, using Cascade Aeration Biofilter or Trickling Filter, Enhancing Solar water Disinfection [(CABFESD)/(CATFESD)] and High-Density Polyethylene lining. This method is highly effective and safe for groundwater and surface water environmental protection. This study could be managing the water poverty for polluted streams and groundwater in the Global South and satisfy the environmental issues to improve water quality and reduce the treatment and health cost in these regions.

Reference Evapotranspiration Modeling Using New Heuristic Methods.

The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.