Development of an analytical method for estimating Manning’s coefficient of roughness for border irrigation

Seyedzadeh, Amin; Panahi, Amir; Maroufpoor, Eisa; Singh, Vijay P.

doi:10.1007/s00271-019-00631-9

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…Initially extracted in vector format, by using the Digital Soils Map of Romania, 1:200,000, this layer was transformed into a grid dataset with a size of cell equal to 30 m. 5d). Initially extracted in vector format, by using the Digital Soils Map of Romania, 1:200000, this layer was transformed into a grid dataset with a size of cell equal to 30 m. Land use, together with the slope angle, controls the surface runoff velocity by influencing the Manning Roughness coefficients and by influencing the rainfall interception process [53][54][55][56]. Forest areas are characterized by high values for both rainfall interception and Manning Roughness coefficients.…”

Section: Flash-flood Predictorsmentioning

confidence: 99%

“…Convergence index (Ci) is a morphometric factor with high importance on the flash-flood potential [18,21]. Its negative values highlight the river valleys perimeters, meanwhile the values Land use, together with the slope angle, controls the surface runoff velocity by influencing the Manning Roughness coefficients and by influencing the rainfall interception process [53][54][55][56]. Forest areas are characterized by high values for both rainfall interception and Manning Roughness coefficients.…”

Section: Flash-flood Predictorsmentioning

confidence: 99%

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Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Costache

Ngo

Bui

2020

Water

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This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

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Section: Flash-flood Predictorsmentioning

confidence: 99%

Section: Flash-flood Predictorsmentioning

confidence: 99%

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Costache

Ngo

Bui

2020

Water

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“…in 2018 were derived from the Qianguo meteorological station (http://data.cma.cn/) (Table 2). Parameters such as roughness, Dalton, viscosity and Stanton number used in the hydrodynamics model were obtained from the literatures [67,68] (Table 2). These datasets of salinity, temperature and water levels between May and October were used for model calibration and validation, respectively.…”

Section: Model Inputsmentioning

confidence: 99%

Effects of Irrigation Discharge on Salinity of a Large Freshwater Lake: A Case Study in Chagan Lake, Northeast China

Liu

Zhang

et al. 2020

Water

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The salinization of freshwater lakes by agricultural activities poses a threat to many lake ecosystems around the world. Quantitative, medium- to long-term studies are needed to understand how some common agricultural practices, such as the discharge of crop irrigation in the vicinities of large lakes, may affect lake salinization. In this study, hydrological, hydrodynamics, water quality and meteorological datasets were used to analyze the long-term spatial-temporal variations of water salinities of a major lake, the Chagan Lake, in Northeast China. An integrated hydrodynamics-salinity model was used to simulate lake water salinity changes taking place at different times and locations, including (i) salt accumulations during a non-frozen period, and (ii) the time when water salinity may reach a significant threshold (1 psu) that jeopardizes a major environmental and economic value of this lake (i.e., the cultivation of local fish species). The results confirmed that Chagan Lake was indeed undergoing salinization in the ten year period between 2008 and 2018. The spatial-temporal patterns of the salinization processes were identified. For instance, (i) the mean salinity of the lake water was found to be 0.55 psu in the summer season of the region and 0.53 psu in the winter, and (ii) between May to October the salinity was up to 0.62 psu in the western region of the lake. The rate of salt accumulation was found to be 97 ton per annum during the non-frozen period. The simulation predicted that by 2024 the lake water will become sub-saline (salinity > 1.07 psu) which is toxic to fish species, if the current practice of irrigation discharge into the lake continues. In the scenario that the amount of irrigation discharges into the lake doubles, the western region of the lake will become sub-saline within one year, and then the whole lake within three years. Overall, this study has produced results that are useful to authorities around the world, for balancing the risks and benefits of developing crop irrigation fields in areas surrounding large freshwater lakes.

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“…In many integral equations, the trend of advance data is important, which cannot be obtained through two points. Furthermore, sometimes the value of r is affected by soil heterogeneity and takes a value greater than unity, which is unacceptable (Elliott & Walker, 1982; Seyedzadeh et al ., 2019). The above method also uses the data of these two points to derive Kostiakov–Lewis infiltration coefficients.…”

Section: Introductionmentioning

confidence: 99%

Investigating the midpoint of a two‐point method for predicting advance and infiltration in surface irrigation^*

Panahi

Seyedzadeh

Maroufpoor

2021

Irrigation and Drainage

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This study used the location of the average advance time (method 1) and the mean infiltration opportunity time (method 2) as the midpoint of the twopoint method for determining "power advance" and Kostiakov-Lewis infiltration parameters. Experiments were carried out in three border-irrigated fields. The results showed that calibration of the power advance equation obtained by the Elliot and Walker method had high accuracy, with an average relative error of 11.3% in the time to complete the advance phase. The root mean square deviation (d RMS ) index used by Elliot and Walker showed that method 1, with an average d RMS value of 15.7 min, has the lowest d RMS , which estimates the advance time with mean d RMS values of 5.1 and 1.8 min less than the proposed method 2 and the method of Elliot and Walker, respectively. Furthermore, using all methods, the Kostiakov-Lewis infiltration equation parameters were determined. In estimating infiltration depth, method 1 had the highest accuracy with a minimum relative error of 0%, a maximum relative error of 8.7%, and an average relative error of 3.8%. Based on both the d RMS index and the accuracy of the infiltration equation, method 1 had a higher accuracy. K E Y W O R D S average advance time, design, evaluation, infiltration opportunity, volume balance Résumé Cette étude a utilisé l'emplacement du temps moyen d'avance (méthode 1) et le temps moyen d'occasion d'infiltration (méthode 2) comme point médian de la méthode à deux points pour déterminer les paramètres d'infiltration "avance de puissance" et Kostiakov-Lewis. Des expériences ont été menées dans trois champs irrigués selon la méthode border irrigation. Les résultats ont montré que l'étalonnage de l'équation d'avance de puissance obtenue par la méthode Elliot et Walker avait une grande précision, avec une erreur relative moyenne de 11,3% dans le temps pour terminer la phase d'avance. L'indice d'écart quadratique moyen ine (d RMS ) utilisé par Elliot et Walker a montré que la * Etude du point médian d'une méthode en deux points pour prédire l'avance et l'infiltration dans l'irrigation de surface

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Development of an analytical method for estimating Manning’s coefficient of roughness for border irrigation

Cited by 15 publications

References 16 publications

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Effects of Irrigation Discharge on Salinity of a Large Freshwater Lake: A Case Study in Chagan Lake, Northeast China

Investigating the midpoint of a two‐point method for predicting advance and infiltration in surface irrigation^*

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Development of an analytical method for estimating Manning’s coefficient of roughness for border irrigation

Cited by 15 publications

References 16 publications

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Effects of Irrigation Discharge on Salinity of a Large Freshwater Lake: A Case Study in Chagan Lake, Northeast China

Investigating the midpoint of a two‐point method for predicting advance and infiltration in surface irrigation*

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Investigating the midpoint of a two‐point method for predicting advance and infiltration in surface irrigation^*