Sediment distribution pattern in some Iranian dams based on a new empirical reservoir shape function

Rahmanian, Mohammad Reza; Banihashemi, Mohammad Ali

doi:10.1080/07438141.2011.602510

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…The functions of the dam are supplying domestic water to the cities of Minab and Bandar Abbas, irrigation water, the artificial recharge of groundwater and flood mitigation. Average sediment deposition in the reservoir was 3.02 million cubic meters per year between 1983 and 2005, presenting 0.9% of annual decreasing volume of the reservoir [37]. The geology of the region is influenced mostly by the coastal Makran and the Zagros Mountain.…”

Section: Study Areamentioning

confidence: 99%

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

et al. 2019

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Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha−1 h−1y−1 in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha−1 y−1, which will generate 5.52 t ha−1 y−1 sediment. The difference between estimated and observed sediment was 1.42 t ha−1 year−1 at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.

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Section: Study Areamentioning

confidence: 99%

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

et al. 2019

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“…The numerical model allows the reservoir-specific partitioning into "coarse" particles that contribute to delta formation and into "fine" particles contributing to sedimentation from homopycnal flows as well as the cut-off of "finest" particles that are wash-load. Furthermore, the longitudinal mass fluxes of suspended sediments can be used to determine the sedimentation patterns, which are far more accurate than the empirical approaches [19][20][21]. Sedimentation patterns arising from sedimentation from homopycnal flows are essential, because they can lead to critical aggradation at intake and outlet structures and thereby endanger operating safety.…”

Section: Discussionmentioning

confidence: 99%

“…The sedimentation patterns can be estimated using conceptual approaches like those in Ref. [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Continuous Seasonal and Large-Scale Periglacial Reservoir Sedimentation

Ehrbar

Schmocker

Doering³

et al. 2018

Sustainability

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Sustainable reservoir use is globally threatened by sedimentation. It has been generally recognized that inadequate consideration of reservoir sedimentation has caused the worldwide decline of net storage. Numerical models are useful tools to simulate sedimentation processes and can be used to derive efficient counter-measures and sediment management strategies. They can be applied to both existing and potential future reservoirs to predict long-term sedimentation. In this study, an application of a simple, robust, and stable numerical 1D model to Gebidem reservoir in Switzerland accompanied by field measurements is presented. It focusses on seasonal and large-scale reservoir sedimentation processes that occur continuously throughout the whole deposition season, while episodic events like turbidity currents are not taken into account. The model simulates both the delta formation of coarse sediments and the lake-wide sedimentation from homopycnal flows. The model is used to assess the effects and significance of varying boundary conditions like inflow, suspended sediment concentration, particle size distribution (PSD), or reservoir operation. It will be demonstrated that future reservoir operation and PSD are as important as future runoff evolution. Based on these findings, implications on future reservoir operation, also considering climate change, are discussed. Finally, an outlook on pending research topics is given.

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“…The Minab dam constructed in 1982 provides water for agricultural use and potable supplies for Bandar Abbas and Minab cities. Due to a high sediment rate (about 3.02 million m 3 /year during 1983–2005), the capacity of the reservoir is decreasing about 0.9% annually 15 . Based on data from the Minab meteorological station, mean annual precipitation ranges between 175 and 365 mm, and the mean annual temperature is 26.9 °C (between 2 and 49 °C).…”

Section: Methodsmentioning

confidence: 99%

Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory

Mohammadifar

Gholami

Golzari

2022

Sci Rep

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This research introduces a new combined modelling approach for mapping soil salinity in the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence limits) and interpretability of two deep learning (DL) models (deep boltzmann machine—DBM) and a one dimensional convolutional neural networks (1DCNN)—long short-term memory (LSTM) hybrid model (1DCNN-LSTM) for mapping soil salinity by applying DeepQuantreg and game theory (Shapely Additive exPlanations (SHAP) and permutation feature importance measure (PFIM)), respectively. Based on stepwise forward regression (SFR)—a technique for controlling factor selection, 18 of 47 potential controls were selected as effective factors. Inventory maps of soil salinity were generated based on 476 surface soil samples collected for measuring electrical conductivity (ECe). Based on Taylor diagrams, both DL models performed well (RMSE < 20%), but the 1DCNN-LSTM hybrid model performed slightly better than the DBM model. The uncertainty range associated with the ECe values predicted by both models estimated using DeepQuantilreg were similar (0–25 dS/m for the 1DCNN-LSTM hybrid model and 2–27 dS/m for DBM model). Based on the SFR and PFIM (permutation feature importance measure)—a measure in game theory, four controls (evaporation, sand content, precipitation and vertical distance to channel) were selected as the most important factors for soil salinity in the study area. The results of SHAP (Shapely Additive exPlanations)—the second measure used in game theory—suggested that five factors (evaporation, vertical distance to channel, sand content, cation exchange capacity (CEC) and digital elevation model (DEM)) have the strongest impact on model outputs. Overall, the methodology used in this study is recommend for applications in other regions for mapping environmental problems.

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Sediment distribution pattern in some Iranian dams based on a new empirical reservoir shape function

Cited by 12 publications

References 2 publications

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

Continuous Seasonal and Large-Scale Periglacial Reservoir Sedimentation

Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory

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