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Ali Talebi
Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd, Iran

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Journal article
Published: 12 June 2021 in CATENA
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Factors involved in hillslope geometry and soil erosion are still under discussion by the scientific community- and one of these factors is microtopography. In this research, the effect of surface roughness (SR) on the soil loss was investigated for different complex hillslope (CHS) systems in terms of plan shapes (convergent, parallel, and divergent) and profile curvatures (convex, concave, and straight), considering representative surface conditions of the arid steep slope region of Tahoneh Watershed, located close to the city of Yazd, Iran. The current research was conducted under laboratory conditions on three different soil types with an SR of 0.015, 0.016, and 0.018 and using a rainfall simulator under a rainfall intensity of 26 ± 3 mm/h with a duration of 15 min. The results showed that the soil loss, as well as the sediment’s arrival time into the outlet of each complex hillslope, varied with SR changes. S, oil loss decreased in each CHS, and the sediment’s arrival time into the outlet was delayed with increased SR in the soil types. A significant difference (F = 51.648, P ≤ 0.001) was obtained in the interaction between the SR and CHSs on the soil loss, as well as the sediment’s arrival time into the outlet. The results of this study using these specific soils indicate that the highest soil loss reduction due to the SR can be observed in straight parallel hillslopes. We conclude that our results can give new key insights about complex geomorphological processes related to sediment mobilization at the pedon scale in arid watersheds to mitigate the negative impacts of human activities on non-straight parallel hillslopes.

ACS Style

Ava Mombini; Nosratollah Amanian; Ali Talebi; Mahboobeh Kiani-Harchegani; Jesús Rodrigo-Comino. Surface roughness effects on soil loss rate in complex hillslopes under laboratory conditions. CATENA 2021, 206, 105503 .

AMA Style

Ava Mombini, Nosratollah Amanian, Ali Talebi, Mahboobeh Kiani-Harchegani, Jesús Rodrigo-Comino. Surface roughness effects on soil loss rate in complex hillslopes under laboratory conditions. CATENA. 2021; 206 ():105503.

Chicago/Turabian Style

Ava Mombini; Nosratollah Amanian; Ali Talebi; Mahboobeh Kiani-Harchegani; Jesús Rodrigo-Comino. 2021. "Surface roughness effects on soil loss rate in complex hillslopes under laboratory conditions." CATENA 206, no. : 105503.

Journal article
Published: 03 December 2019 in Water
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The geometry of hillslopes (plan and profile) affects soil erosion under rainfall-runoff processes. This issue comprises of several factors, which must be identified and assessed if efficient control measures are to be designed. The main aim of the current research was to investigate the impact of surface Roughness Coefficients (RCs) and Complex Hillslopes (CHs) on runoff variables viz. time of generation, time of concentration, and peak discharge value. A total of 81 experiments were conducted with a rainfall intensity of 7 L min−1 on three types of soils with different RCs (i.e., low = 0.015, medium = 0.016, and high = 0.018) and CHs (i.e., profile curvature and plan shape). An inclination of 20% was used for three replications. The results indicate a significant difference (p-value ≤ 0.001) in the above-mentioned runoff variables under different RCs and CHs. Our investigation of the combined effects of RCs and CHs on the runoff variables shows that the plan and profile impacts are consistent with a variation in RC. This can implicate that at low RC, the effect of the plan shape (i.e., convergent) on runoff variables increases but at high RC, the impact of the profile curvature overcomes the plan shapes and the profile curvature’s changes become the criteria for changing the behavior of the runoff variables. The lowest mean values of runoff generation and time of concentration were obtained in the convex-convergent and the convex-divergent at 1.15 min and 2.68 min, respectively, for the soil with an RC of 0.015. The highest mean of peak discharge was obtained in the concave-divergent CH in the soil with an RC of 0.018. We conclude that these results can be useful in order to design planned soil erosion control measures where the soil roughness and slope morphology play a key role in activating runoff generation.

ACS Style

Masoud Meshkat; Nosratollah Amanian; Ali Talebi; Mahboobeh Kiani-Harchegani; Jesús Rodrigo-Comino. Effects of Roughness Coefficients and Complex Hillslope Morphology on Runoff Variables under Laboratory Conditions. Water 2019, 11, 2550 .

AMA Style

Masoud Meshkat, Nosratollah Amanian, Ali Talebi, Mahboobeh Kiani-Harchegani, Jesús Rodrigo-Comino. Effects of Roughness Coefficients and Complex Hillslope Morphology on Runoff Variables under Laboratory Conditions. Water. 2019; 11 (12):2550.

Chicago/Turabian Style

Masoud Meshkat; Nosratollah Amanian; Ali Talebi; Mahboobeh Kiani-Harchegani; Jesús Rodrigo-Comino. 2019. "Effects of Roughness Coefficients and Complex Hillslope Morphology on Runoff Variables under Laboratory Conditions." Water 11, no. 12: 2550.

Journal article
Published: 01 December 2019 in Journal of Water and Soil Science
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Flood is a natural disaster making the heavy humanistic and economic damages each year in most parts of Iran. In this research, the SWAT model performance in flood prediction and sub-basin priority was investigated in terms of flooding in Araz-Kose watershed in Golestan province. To calibrate the model, SUFI2 was applied. ...

ACS Style

A. Talebi; E. Abyari; S. Parvizi. Prioritization of Sub-Watersheds from Flooding Viewpoint Using the SWAT Model (Arazkoose Watershed, Golestan Province). Journal of Water and Soil Science 2019, 23, 409 -419.

AMA Style

A. Talebi, E. Abyari, S. Parvizi. Prioritization of Sub-Watersheds from Flooding Viewpoint Using the SWAT Model (Arazkoose Watershed, Golestan Province). Journal of Water and Soil Science. 2019; 23 (4):409-419.

Chicago/Turabian Style

A. Talebi; E. Abyari; S. Parvizi. 2019. "Prioritization of Sub-Watersheds from Flooding Viewpoint Using the SWAT Model (Arazkoose Watershed, Golestan Province)." Journal of Water and Soil Science 23, no. 4: 409-419.

Original article
Published: 22 April 2019 in Sustainable Water Resources Management
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Underground dams are constructions that prevent the natural flow of ground water and create underground water resources. Although lateral-flow volume is one of the main criteria to locate an underground dam, this factor is ignored in the most studies. In this research, the water balance equation was simulated using the SWAT model in the Daroongar watershed (northeastern Iran), and the paths of subsurface water flows were identified. Continuing by giving the weight to an 11-layer analytical network process (ANP), potential suitable locations for the construction of underground dams were determined. With the implementation of the SWAT model in the Daroongar watershed, R2, bR2, and NS (Nash Satklyf) coefficients were 0.77, 0.75, and 0.68, respectively, in the calibration phase; in the validation phase, they were 0.71, 0.67, and 0.61 in the same order. All these coefficients show that the efficiency of the SWAT model in simulating water balance is acceptable. Results of the sensitivity analysis of the SWAT model show 27 effective parameters, where the runoff curve number (CN2) was identified as the most important parameter. The results of the ANP model prove that subsurface flow, weighing 0.131, includes the most weight allocated. Results from the ANP model indicated that this is necessary for determining the extent of subsurface flow using the SWAT model. In addition, the reservoir volume and runoff volume, respectively, with a weight of 0.109 and 0.101 are more important than other factors. As a result, the greater the current subsurface stream, the more important it is in relation with other streams. The results showed that streams with ranks 3 and 4, which are located on slopes less than 15%, are suitable for the construction of underground dams.

ACS Style

Ali Talebi; Ehsan Zahedi; Marwan A. Hassan; Mohammad Taghi Lesani. Locating suitable sites for the construction of underground dams using the subsurface flow simulation (SWAT model) and analytical network process (ANP) (case study: Daroongar watershed, Iran). Sustainable Water Resources Management 2019, 5, 1369 -1378.

AMA Style

Ali Talebi, Ehsan Zahedi, Marwan A. Hassan, Mohammad Taghi Lesani. Locating suitable sites for the construction of underground dams using the subsurface flow simulation (SWAT model) and analytical network process (ANP) (case study: Daroongar watershed, Iran). Sustainable Water Resources Management. 2019; 5 (3):1369-1378.

Chicago/Turabian Style

Ali Talebi; Ehsan Zahedi; Marwan A. Hassan; Mohammad Taghi Lesani. 2019. "Locating suitable sites for the construction of underground dams using the subsurface flow simulation (SWAT model) and analytical network process (ANP) (case study: Daroongar watershed, Iran)." Sustainable Water Resources Management 5, no. 3: 1369-1378.

Articles
Published: 04 July 2018 in Hydrological Sciences Journal
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This research focused on the determination of land cover thresholds that have a significant impact on runoff generation and soil loss at the pedon scale. For this purpose, six erosion micro-plots were set up on grassland and shrubland types of rangeland in the northeast of Iran, and the amounts of vegetation cover, litter, runoff and soil loss on them were measured. A factorial statistical analysis was carried out on the completely randomized design using land cover and rainfall factors. The results show that the effect of rainfall on soil loss and runoff was greater than that of land cover. Also, the effect of land cover on soil loss was greater than that on runoff generation. Furthermore, two specific thresholds were identified: the first was from 10 to 30% of landcover and the second from 50 to 70%.

ACS Style

Masoud Eshghizadeh; Ali Talebi; Mohammadtaghi Dastorani. Thresholds of land cover to control runoff and soil loss. Hydrological Sciences Journal 2018, 63, 1424 -1434.

AMA Style

Masoud Eshghizadeh, Ali Talebi, Mohammadtaghi Dastorani. Thresholds of land cover to control runoff and soil loss. Hydrological Sciences Journal. 2018; 63 (9):1424-1434.

Chicago/Turabian Style

Masoud Eshghizadeh; Ali Talebi; Mohammadtaghi Dastorani. 2018. "Thresholds of land cover to control runoff and soil loss." Hydrological Sciences Journal 63, no. 9: 1424-1434.

Journal article
Published: 19 October 2016 in International Journal of Remote Sensing
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The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the Ts–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, Ts, Fvegetation, Fsoil, temperature (T), precipitation at time t (Pt, Pt – 1, Pt – 2), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between −2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.

ACS Style

Vahid Moosavi; Ali Talebi; Mohammad Hossein Mokhtari; Mohammad Reza Hadian. Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches. International Journal of Remote Sensing 2016, 37, 5605 -5631.

AMA Style

Vahid Moosavi, Ali Talebi, Mohammad Hossein Mokhtari, Mohammad Reza Hadian. Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches. International Journal of Remote Sensing. 2016; 37 (23):5605-5631.

Chicago/Turabian Style

Vahid Moosavi; Ali Talebi; Mohammad Hossein Mokhtari; Mohammad Reza Hadian. 2016. "Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches." International Journal of Remote Sensing 37, no. 23: 5605-5631.

Case studies
Published: 12 October 2016 in ISH Journal of Hydraulic Engineering
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Estimation of suspended sediment load is one of the important topics in river engineering. Different methods are used for estimating the sediment rate. In recent years, different artificial intelligence (AI) methods, such as artificial neural network (ANN), have been used for the estimation of sediments in rivers. In this research, the suspended sediment load has been studied by using regression trees (RTs) and model trees (MTs). The study area has been located in Hyderabad watershed in west of Iran. The input data included the flow discharge, sum of three days discharge, sum of five days precipitation and the suspended sediment discharge were considered as output in the models. The numbers of total data of sediment discharge was 223 records. The obtained results were compared with ANN method (feed forward back propagation algorithm) and sediment rating curve (SRC). Results showed that RT and MT outperformed ANN method in the study area. The method of SRC had high accuracy for daily sediment discharge less than 100 ton per day in comparison with AI models, while the AI models had higher accuracy for high sediment discharge. Moreover, the combination of artificial intelligent models had high accuracy regarding to each model lonely.

ACS Style

Ali Talebi; Javad Mahjoobi; Mohammad Taghi Dastorani; Vahid Moosavi. Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). ISH Journal of Hydraulic Engineering 2016, 23, 1 -8.

AMA Style

Ali Talebi, Javad Mahjoobi, Mohammad Taghi Dastorani, Vahid Moosavi. Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). ISH Journal of Hydraulic Engineering. 2016; 23 (2):1-8.

Chicago/Turabian Style

Ali Talebi; Javad Mahjoobi; Mohammad Taghi Dastorani; Vahid Moosavi. 2016. "Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran)." ISH Journal of Hydraulic Engineering 23, no. 2: 1-8.

Journal article
Published: 29 January 2016 in Hydrological Processes
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ACS Style

Ali Talebi; Rasoul Hajiabolghasemi; Mohamad Reza Hadian; Nosratollah Amanian. Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes. Hydrological Processes 2016, 30, 1968 -1977.

AMA Style

Ali Talebi, Rasoul Hajiabolghasemi, Mohamad Reza Hadian, Nosratollah Amanian. Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes. Hydrological Processes. 2016; 30 (12):1968-1977.

Chicago/Turabian Style

Ali Talebi; Rasoul Hajiabolghasemi; Mohamad Reza Hadian; Nosratollah Amanian. 2016. "Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes." Hydrological Processes 30, no. 12: 1968-1977.

Journal article
Published: 01 November 2015 in Remote Sensing of Environment
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ACS Style

Vahid Moosavi; Ali Talebi; Mohammad Hossein Mokhtari; Seyed Rashid Fallah Shamsi; Yaghoub Niazi. A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sensing of Environment 2015, 169, 243 -254.

AMA Style

Vahid Moosavi, Ali Talebi, Mohammad Hossein Mokhtari, Seyed Rashid Fallah Shamsi, Yaghoub Niazi. A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sensing of Environment. 2015; 169 ():243-254.

Chicago/Turabian Style

Vahid Moosavi; Ali Talebi; Mohammad Hossein Mokhtari; Seyed Rashid Fallah Shamsi; Yaghoub Niazi. 2015. "A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature." Remote Sensing of Environment 169, no. : 243-254.

Journal article
Published: 01 January 2014 in Geomorphology
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ACS Style

Vahid Moosavi; Ali Talebi; Bagher Shirmohammadi. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology 2014, 204, 646 -656.

AMA Style

Vahid Moosavi, Ali Talebi, Bagher Shirmohammadi. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology. 2014; 204 ():646-656.

Chicago/Turabian Style

Vahid Moosavi; Ali Talebi; Bagher Shirmohammadi. 2014. "Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method." Geomorphology 204, no. : 646-656.

Journal article
Published: 14 February 2013 in Journal of Hydroinformatics
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Estimation of the design flood flow for hydraulic structures is often performed by adjusting probabilistic models to daily mean flow series. In most cases, this may cause under design of the structure capacity with possible risks of failure because instantaneous peak flows may be considerably larger than the daily averages. As there is often a lack of instantaneous flow data at a given site of interest, the peak flow has to be estimated. This paper develops new machine-learning-based methods to estimate the instantaneous peak flow from mean daily flow data where long daily data series exist but the instantaneous peak data series are short. However, the presented methods cannot be used where only daily flow data are available. Developed methodologies have been successfully applied to series of flow information from different gauging stations in Iran, with important improvements compared to traditional empirical methods available in the literature. Reliable results produced by the machine-learning-based models compared to the traditional methods show the superior ability of these techniques to solve the problem of inadequate measured peak flow data periods, especially in developing countries where it is difficult to find sufficiently long instantaneous peak flow data series.

ACS Style

Mohammad T. Dastorani; Jamile Salimi Koochi; Hamed Sharifi Darani; Ali Talebi; M. H. Rahimian. River instantaneous peak flow estimation using daily flow data and machine-learning-based models. Journal of Hydroinformatics 2013, 15, 1089 -1098.

AMA Style

Mohammad T. Dastorani, Jamile Salimi Koochi, Hamed Sharifi Darani, Ali Talebi, M. H. Rahimian. River instantaneous peak flow estimation using daily flow data and machine-learning-based models. Journal of Hydroinformatics. 2013; 15 (4):1089-1098.

Chicago/Turabian Style

Mohammad T. Dastorani; Jamile Salimi Koochi; Hamed Sharifi Darani; Ali Talebi; M. H. Rahimian. 2013. "River instantaneous peak flow estimation using daily flow data and machine-learning-based models." Journal of Hydroinformatics 15, no. 4: 1089-1098.