| 1. |
- Bhat, Shakeel Ahmad, et al.
(författare)
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Application of Biochar for Improving Physical, Chemical, and Hydrological Soil Properties: A Systematic Review
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:17
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Forskningsöversikt (refereegranskat)abstract
- Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility.
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| 2. |
- Dahmani, Abdennasser, et al.
(författare)
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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction
- 2023
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Ingår i: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 77:2, s. 2579-2594
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Tidskriftsartikel (refereegranskat)abstract
- Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic zones were used to develop the model. Various error measurements were used to determine the accuracy of the prediction models, including the correlation coefficient, the mean absolute error, and the root mean square error (RMSE). The optimal support vector machine (SVM) model showed exceptional efficiency during the training phase, with a high correlation coefficient (R = 0.99) and a low mean absolute error (MAE = 26.5741 Wh/m2), as well as an RMSE of 38.7045 Wh/m² across all phases. Overall, this study highlights the importance of accurate prediction models in the renewable energy, which can contribute to better energy management and planning.
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| 3. |
- Djaafari, Abdallah, et al.
(författare)
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Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
- 2022
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Ingår i: Energy Reports. - : Elsevier. - 2352-4847. ; 8, s. 15548-15562
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Tidskriftsartikel (refereegranskat)abstract
- Although solar energy harnessing capacity varies considerably based on the employed solar energy technology and the meteorological conditions, accurate direct normal irradiation (DNI) prediction remains crucial for better planning and management of concentrating solar power systems. This work develops hybrid Long Short-Term Memory (LSTM) models for assessing hourly DNI using meteorological datasets that include relative humidity, air temperature, and global solar irradiation. The study proposes a unique hybrid model, combining a balance-dynamic sine–cosine (BDSCA) algorithm with an LSTM predictor. Combining optimizers and predictors, such hybrid models are rarely developed to estimate DNI, especially in smaller prediction intervals. Therefore, various commonly adopted algorithms in relevant studies have been considered references for evaluating the new hybrid algorithm. The results show that the relative errors of the proposed models do not exceed 2.07%, with a minimum correlation coefficient of 0.99. In addition, the dimensionality of inputs was reduced from four variables to the two most cost-effective variables in DNI prediction. Therefore, these suggested models are reliable for estimating DNI in the arid desert areas of Algeria and other locations with similar climatic features.
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| 4. |
- Gupta, Sanjeev, et al.
(författare)
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Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network
- 2024
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:6
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Tidskriftsartikel (refereegranskat)abstract
- Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.
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| 5. |
- Gururani, Dheeraj Mohan, et al.
(författare)
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Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods
- 2023
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Ingår i: Water. - : MDPI. - 2073-4441. ; 15:22
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Tidskriftsartikel (refereegranskat)abstract
- The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas.
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| 6. |
- Jamei, Mehdi, et al.
(författare)
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Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions
- 2023
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Ingår i: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 74:1, s. 1625-1640
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Tidskriftsartikel (refereegranskat)abstract
- Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968–0.988 and RMSE = 1.274–1.403 MJ/m2⋅d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR. Hence, this study recommends new effective methods to predict GSR in semi-arid regions.
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| 7. |
- Kumar, Rohitashw, et al.
(författare)
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A review on emerging water contaminants and the application of sustainable removal technologies
- 2022
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Ingår i: Case Studies in Chemical and Environmental Engineering. - : Elsevier. - 2666-0164. ; 6
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Forskningsöversikt (refereegranskat)abstract
- Emerging contaminants (ECs) are synthetic or naturally occurring chemicals or any microorganisms that are not commonly monitored in the environment but have the potential to enter the environment and cause known or suspected adverse ecological or human health effects. The issue of ECs persistent in the environment and can disrupt the physiology of target receptors, they are recognized as Contaminants of emerging environmental concerns. The prominent classes of ECs include pharmaceuticals and personal care products (PPCPs), plasticizers, surfactants, fire retardants, nanomaterials, and pesticides. Several ECs have been recognized as endocrine disruptive compounds (EDCs) due to their deleterious effects on endocrine systems (EDCs). The contaminants present in the aquatic environment resources are a major cause of concern for human health and the environment and safety concern. These contaminations have risen into a major threat to the water distribution system. The impact of emerging contaminants (ECs) such as medicines, x-ray media, endocrine disruptors, insecticides, and personal care items has been reported in surface water, wastewater, and groundwater sources worldwide in recent years. Various techniques have been explored for ECs degradation and removal to mitigate their harmful effect. Numerous prior or continuing investigations have focused on the degradation and removal of contaminants using a variety of treatment techniques, including (1) physical, (2) chemical, and (3) biological. However, experimental data is insufficient to provide precise predictions regarding the mechanistic degradation and removal fate of ECs across various in-practice systems. The membrane technology can remove particles as fine as 10 μm and colloidal particles, It can be effectively eliminated by up to 99% through the use of MBR and treatment technologies such as reverse osmosis, ultrafiltration, or nanofiltration at concentrations up to 5 g/liter. In this paper, the emerging contaminants overview, their sources, and their removal by application of various treatments based on recent studies have been presented.
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| 8. |
- Kumar, Rohitashw, et al.
(författare)
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Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The Dv and R2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2◦ C, and (c) a temperature rise of +2◦ C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increased by 53% in summer (April–September). In contrast, the projected increased discharge for scenarios (a) and (c) was 37% and 67%, respectively. The SRM efficiently forecasts future water supplies due to snowmelt runoff in high elevation, data-scarce mountain environments.
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| 9. |
- Kumar Singh, Abhinav, et al.
(författare)
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An Integrated Statistical-Machine Learning Approach for Runoff Prediction
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.
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| 10. |
- Kumari, Arti, et al.
(författare)
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Estimation of Actual Evapotranspiration and Crop Coefficient of Transplanted Puddled Rice Using a Modified Non-Weighing Paddy Lysimeter
- 2022
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Ingår i: Agronomy. - : MDPI. - 2073-4395. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies are costly, laborious, and require skills which make in situ ET estimation difficult, particularly in developing countries. With this in mind, an attempt was made to determine ETa and stagewise crop coefficient (Kc) values of transplanted puddled rice using a modified non-weighing paddy lysimeter. The results were compared to indirect methods, viz., FAO Penman–Monteith and pan evaporation. Daily ETa ranged from 1.9 to 8.2 mmday−1, with a mean of 4.02 ± 1.35 mmday−1, and their comparison showed that the FAO Penman–Monteith equation performed well for the coefficient of determination (R2 of 0.63), root mean squared error (RMSE = 0.80), and mean absolute percentage error (MAPE = 13.6 %), and was highly correlated with ETa throughout the crop season. However, the pan evaporation approach was underestimated (R2 of 0.24; RMSE = 0.98; MAPE = 22.13%) due to a consistent pan coefficient value (0.71), vegetation role and measurement errors. In addition, actual Kc values were obtained as 1.13 ± 0.13, 1.27 ± 0.2, 1.23 ± 0.16, and 0.93 ± 0.18 for the initial, crop development, mid-season, and end-season stages, respectively. These estimated crop coefficient values were higher than FAO Kc values. Statistical analysis results revealed that the overall stagewise-derived average Kc values were in line with FAO values, but different from the derived pan Kc values, although found insignificant at a 5% significance level. In addition, water productivity and agro-meteorological indices were derived to evaluate the cultivar performance in this experiment. Therefore, such a methodology may be used in the absence of weighing lysimeter-derived Kc values. The derived regional Kc values can be applied to improve irrigation scheduling under similar agro-climatic conditions.
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| 11. |
- Parvaze, Sabah, et al.
(författare)
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Optimization of Water Distribution Systems Using Genetic Algorithms: A Review
- 2023
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Ingår i: Archives of Computational Methods in Engineering. - : Springer. - 1134-3060 .- 1886-1784. ; 30, s. 4209-4244
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Forskningsöversikt (refereegranskat)abstract
- Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms.
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| 12. |
- Ramulu, Chelpuri, et al.
(författare)
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A residue management machine for chopping paddy residues in combine harvested paddy field
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 13. |
- Sharma, Vipasha, et al.
(författare)
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Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices
- 2022
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Ingår i: Atmosphere. - : Mdpi. - 2073-4433. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010-2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010-2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010-2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (-0.24, -0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning.
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| 14. |
- Singh, Sachin Kumar, et al.
(författare)
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Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions
- 2023
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Ingår i: Mathematical Biosciences and Engineering. - : American Institute of Mathematical Sciences. - 1551-0018. ; 20:6, s. 11403-11428
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Tidskriftsartikel (refereegranskat)abstract
- 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%.
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| 15. |
- Vishwakarma, Dinesh Kumar, et al.
(författare)
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Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 16. |
- Vishwakarma, Dinesh Kumar, et al.
(författare)
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Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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