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- Taylor & Francis
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- © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).
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- 2474-9508
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- 10.1080/24749508.2022.2163618
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Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2163618 RESEARCH ARTICLE Investigating effects of mining on sedimentary properties of Lisar River (Guilan Province, Iran) using HEC_RAS model a a b Seyed Ahmad Hosseini , Mohammadreza Gharibreza and Alireza Ghodrati Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran; The Natural and Watershed Management Department, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran ABSTRACT ARTICLE HISTORY Received 1 September 2022 Increasing land development projects lead to demand for riverine mining, followed by erosion Accepted 26 December 2022 and deposition. The aim of the research was to assess the sedimentary impacts of mining activities on the Lisar River. The research material comprised environmental, hydraulic, and KEYWORDS sedimentary information, mining history, and dimensions. Detailed topographic and TIN maps, HEC-RAS; gravel mining; and 55 cross-sections of the Lisar River mining, were prepared. This study simulates flow river; sedimentation; patterns in a quasi-unsteady condition and sediment transport capacity using the HEC-RAS simulation model. The maximum change along the longitudinal profile of the river mining is 3 m. The spatial map of the mining in different river sections was determined based on the maximum allowable depth of mining. The present research recommends the Yang function use in simulations of the rivers with sandy-gravel texture accompanied with or without riverine mining and steep slope. Results indicate the current mining volume is up to three times the allowed capacity for the extraction from the Lisar River. The present research concluded that the management plan for spatial mining and measures for monitoring based on the spatial distribution of depositional and erosional sites is necessary for this and other such areas to conserve natural resources. 1. Introdution Wishart et al. (2008) highlighted the impacts of Rivers are a dynamic system encountering continuous changes due to human uses. According to the research mining operations on the Wolsingham and Harper assumptions, mining operations have several sedi- rivers (UK) as effective human interventions and mentological and hydrological impacts on a river sys- changes in river morphology. They examined the tem, which lead to imbalances in the ecosystem effects of mining on the River Wear in the north of services. A literature review on the subjects involved England during the 1930s and 1960s. They showed in the present research led us to two groups of pre- a significant difference in the morphological charac- vious works regarding the impacts of a riverine mining teristics of the river over 30 years and changes in the operation on water and sediment regimes and meth- shape and form of the river. ods to estimate changes in two such processes. The study of Gómez-Álvarez et al. (2011), The implications of mining activities are rapid spa- Ushakova et al. (2022), and Sellier et al. (2021) show tial relocation of channels, riverbank erosion, destruc- the short- and long-term impacts of mining on the tion of hydraulic structures, and relevant socio- contamination of natural resources. These three stu- economic reactions (Sracek et al., 2012; Thi Kim dies focused on the implications of mining on the et al., 2020; Sellier et al. 2021; Ushakova et al. composition of on-site and off-site sediments that 2022Wishart et al., 2008; Gómez-Álvarez et al. are disturbed and transported from excavation sites. 2011;). Further, mining from river banks resulted in They applied geochemical indicators and sediment the widening of the river channel, an increase in dis- quality indices to estimate such impacts. charge efficiency, and the passing of floods. Indeed, Thi Kim et al. (2020) simulated the effects of gravel this process has advantages in terms of the river mining during a 20 years time range on a river sedi- hydraulic, but continuous mining on river banks will mentary regime. They found that the erosion of river destroy surrounding lands. In these conditions, recog- channels’ trend in the gravel mines is faster than nizing and analyzing the hydraulic parameters of flow ranges without gravel mining. Sracek et al. (2012) and sediment is the foundation for studying the beha- measured the discharge of sediment and dissolved vior of the river and deciding on engineering practices. load (toxic elements) as the impact of mining on the CONTACT Seyed Ahmad Hosseini sahosseini@yahoo.com Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran 1389817611, Iran © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 S. A. HOSSEINI ET AL. Kafue River in Zambia. As a result, an increase is at the 5% error level were not significantly different observed in the volume of suspended loads and values from the measured data, while the Meyer–Peter– of copper, cobalt, and manganese and their adverse Muller, Wilcox, Eykers–White, and Larsen methods effects on the downstream environment. have different values in comparison with the observed The amount and spatial pattern of erosional and values. Azizian and Samadi (2019) investigated the depositional sites along a river are essential to study potential of off-site river mining by combining GIS the behavior of the river and to predict possible changes. and geomorphological models in Ferdows and Ghaen Accordingly, some numerical models based on the watersheds. Their results showed that geomorphologi- mathematical solution of transferring, distribution, sedi- cal models could identify the source of sediment pro- mentation, and digging were introduced by researchers duction. River stability and navigation safety in the to understand such phenomena (Asada, 1973; Asadi Yangtze River regarding gravel excavation upstream of et al., 2018; Azizian & Samadi, 2019; Gibson et al., the river were studied by Xiao et al. (2022). Accordingly, 2006; Haghiabi et al., 2005; Holly, 1990; Moradinejad a two-dimensional hydrodynamic-sediment transport et al., 2014; Peirov et al., 2011). numerical model predicted the evolution of the mined Asada (1973) proposed a mathematical model channel. Her results indicate that gravel mining distri- (ASADA) for calculating sediment transport in moun- butes among the following nine river sections. tainous rivers and reservoirs that considers the water In a study, Moradinejad and Hoseini (2022) investi- profile at the initial slope of the bed via steady and non- gated the effect of sand removal from the Qara_Chai uniform equations. The rate of change in the bed over River basin. They concluded that the river plan would some time is calculated using the sediment transport be changed by continuing to take sand from the river equation and the sediment continuity equation. Holly without management practices and monitoring. (1990) proposed a model for simulating the non- Moradi Chonghoralu et al. (2022) compared mountai- continuous flow and sediment motion in a network of nous and riverine sand mines in Urmia City in sedi- moveable channels with a transportable bedload. This ment size distribution. The comparison evaluated the model is capable of routing the hydraulic process but capability of the Weibull, Fredland, Van Gnochten, and cannot simulate the sticky sediment movement and the Jackie models. Six efficiency coefficients tested the effects of debris flows. Haghiabi et al. (2005) conducted accuracy of models of sediment particles. Reviewing a sediment transport simulation using the experimental this work showed a minor difference between wash- method. They concluded that this phenomenon is loads value and particle size distribution of sand mining mainly dependent on a parameter called Richardson on mountainous and riverine sides of the watershed. number and bed slope angle. The recommended math- Further, Momeneh (2022) compared the perfor- ematical models are able in estimating sedimentation in mance of artificial intelligence models with the rivers and dam reservoirs to some extent. However, no IHACRES model in modeling the flow of the effort was reported to determine the location of suitable Gamasiab River watershed. Models’ outputs showed sites for sediment removal. Difficulty in solving the that without management practices, sand mining equations of analytical and numerical methods in dif- caused a change in the course of the river and the ferent cases causes complexity in the river’s hydraulic pattern of erodibility and sedimentation in the river. and sedimentary problems modeling. For this purpose, According to the research objectives and reviewed Gibson et al. (2006) examined the capability of the literature, the present study planned to reveal the the- HEC-RAS model for calculating sediment transport in matic and spatial impacts of mining operations in the rivers and compared the results of this model with the Lisar River and sediment transport and sedimentation Hec-6 model. They concluded that the HEC-RAS using the HEC-RAS model. Another aim of this model has well performance in the simulations, but research was to present a material extraction program the results of this model were slightly different from for sustainable future mining. Allocation of resources the HEC-6 in specific conditions raised by differences in or non-issuance of legal permits to manage the the hydraulic relationships of the model. Asadi et al. removal of sediment from river beds by mining com- (2018) showed that among the sediment transport panies was a side purpose of this research. equations in the HEC-RAS model, the Mear–Peter and Muller equation in the Talar River is the most 2. Materials and methods consistent with reality, and it is suitable for estimating changes in cross-sections along the river. Another work 2.1. The study area (Peirov et al., 2011) emphasized the ability of the HEC- RAS model to calculate the average volume of sediment Lisar watershed has a 206.5 km area and the west discharge from the rivers with mining ranges. of Guilan province, which drains to the Caspian Moradinejad et al. (2014) investigated the sediment Sea. The watershed is covered mainly by the transport from the Poledoab station of the Shera River Hircanian mixed forest. The mean annual tem- in which the Engelund-Hansen and Toffalti equations perature and rainfall values according to a 29 - GEOLOGY, ECOLOGY, AND LANDSCAPES 3 years data (1989–2018) are 15.8°C and 653 mm. 2.2. Field observations However, the rainfall value in the coastal zone is The topographic information, the long-term data of 1026 mm per year. The main waterway is called flow and sediment discharge, sediment characteristics, the Lisar River, which has a 22 km length with and mining history and dimensions were used as the a west to east direction, and a slope of 0.02%. research materials. Surveying covered the mining area Mining and agriculture are two main land use with a 1:2,000 scale along the 4 km of the Lisar River. along the Lisar River before draining to the AutoCAD software version seven was used to prepare Caspian Sea. To achieve the research purpose, target maps (Figure 2). All maps are exported to the 4 km from the mining-affected range of this ArcGIS software version 10.2 to make GIS-Ready river is considered for investigations. Currently, 3 layers. A deterministic strategy controls the sampling seven mines produce 90,000 m of extracted mate- strategy according to affected positions by mining. rials per year. The hydrometric station of this Therefore, sediment samples were also taken before river is called Siah Jafar, located at mid point of and after the mining range. A total of 20 samples were the selected reach (Figure 1). According to the analyzed for determining grain size distribution and processed data (1989–2018), the base and maxi- bulk density. Grain size analysis was implemented mum discharge of the Lisar River are 0.18 and 82 3 −1 using the ASTM 422 method and the ASTM D7928 m s , respectively. Figure 1. Location of Lisar River and mining sites. Figure 2. (a) showing effects of mining on river morphology, (b) widening of river channel of the Lisar River due to mining. 4 S. A. HOSSEINI ET AL. Figure 3. A sample of the Lisar river bed sediment size distribution. method to determine the distribution of course and - D is the total diameter of the particles (mm); - Dm is the average diameter of the particles or d fine particle size portions, respectively. In addition to (mm); sampling, field observations focused on other envir- - s is the specific gravity of the sediment particles; onmental issues induced by mining, such as bank −1 - V is the average velocity in the channel (ft s ); erosion, river bed scouring, and other lateral - S is the energy line slope, implications. - W is the channel depth (ft); - T is the water temperature (°F); - R is the hydraulic radius (ft). 2.3. Procedures for the model setup This model is developed based on the sediment trans- port and the sediment continuity equations under 2.4. Data used in a hydraulic simulation of river one-dimensional conditions and the assumption of flow and sediment quasi-non-steady flow (Lorang & Aggett, 2005). The Three types of following data are used to simulate the HEC-RAS model uses seven different functions to hydraulic flow and sediment of the river using the simulate one-dimensional sediment deposition and HEC-RAS model. bed scouring, sediment transport trend using the Exner equation. The law of conservation of mass or principle of mass conservation states (Exner equa- 2.5. Geometrical characteristics and coefficients tion, 1) for sediment is presented by USACE (2020). roughness of river @η @Q The geometric data includes the prepared TIN map of ð1 λ ÞB ¼ (1) @t @x the Lisar River affected by mining imported to the where B is the width of the river (m), η is bed HEC-GeoRAS package in ArcMap. The river plan elevation relative to some fixed datum (m), the poros- and 55 cross-sections along the selected range (4 km) ity of the active layer, Q the discharge of the carried were prepared and transferred to the HEC-RAS sediment load (m /day), x is the distance (m), and t is model. the time (s). The prepared equations in the model help The manning roughness coefficient is the function the user select the appropriate modeling with river of field observations (particle size distribution, vegeta- situations. The specifications of the functions used in tion density, channel morphology) and valid tables the HEC-RAS software are presented in Table 1. released by Hosseini and Abrishami (2004) and Sediment size distribution (Fig. 3) showed that up Shafaei-Bejestan (2008). Further, the Siahjafar hydro- to55 % of the bed materials are composed of gravel- metric station and its hydraulic characteristics applied sized particles, whilesand-sized particles have about 15 through an inverse solution of the Manning equation frequencies. helped to calculate this coefficient. Classifying the GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Table 1. Specifications of sediment functions used in HEC-RAS software. How to References extract Grading Description Terms of use of the relationship Ackers-White Laboratory Sand-gravel Suspended load transfer (fines) is a function of fluctuations in water 0.04<d<7 mm, 0.07<v<7.1 (fps) (1973) flow turbulence. bed load transfer (coarse-grained) is a function of 0.00006<s<0.0037, 1<Gs<2.7 shear stress applied to sediments 46<T<89 °F 0.01<D<1.4 ft, 0.23<w<4 ft Engelund and Laboratory Sand It is used for sandy rivers with significant suspended loads 0.19<d<0.93 mm, 0.65<v<6.34 Hansen (fps) 0.000055<s<0.019, (1972) 45<T<93 °F 0.19<D<1.33 ft Laursen Laboratory Silt-gravel It is used for rivers with sandy loads 0.011<d<29 mm, 0.7<v<9.4 (fps) (1958) 0.00025<s<0.025, 0.25<w<6.6 ft 46<T<83 °F 0.01<D<1.4 ft Meyer–Peter– Laboratory Sand-gravel It is used for rivers that have coarse sediments 0.04<d<7 mm, 1.2<v<9.4 (fps) Muller 0.0004<s<0.02, (1948) 46<T<89 °F 0.03<D<3.9 ft, 0.5<w<6.6 ft Toffaleti Laboratory Sand Sediment discharge is estimated based on the calculation of 0.062<d<4 mm, 0.7<v<7.8 (fps) (1968) concentration columns (divided into 4 zones) 0.000002<s<0.0011, 63<w<3640 ft 32<T<9 °F 0.095<D<0.76 ft Yang (1984), Laboratory Sand-gravel Estimation of sediment discharge rate is based on flow strength 0.15<d<1.7 mm, 0.8<v<6.4 (fps) 0.000043<s<0.028, 0.04<D<50 ft 32<T<94 °F, 0.23<w<4 ft Wilcock and Laboratory Sand-gravel Used to determine bed load and sand transfer potential – Crowe >2 mm (2003) long-term discharge of the Lisar River provided pos- representative station provided the boundary condi- sible water elevation levels. Optimizing the manning tion. In addition to sediment load data, granulometric coefficient was performed by applying the time series characteristics of the riverbed materials are used in the of the river discharge and water level using the HEC- simulation of the model. RAS model. Accordingly, the manning coefficient for Another specific data were the moveable thickness the flood plain and the active river bed are 0.052 and of the river bed at each cross-section along the 4 km 0.049, respectively. selected range. Such a depth of movable materials was The mining operation resulted in the divergence measured during field observations aided by geologi- and convergence of the river channel. Such processes cal evidence. As a result, the maximum moveable indicate the trend of flow energy loss, which is defin- depth for the simulation procedure is between 2.5 able by the convergence (Cc) and divergence coeffi- and 3 m. The present research assumed that moveable cients (Ce). These coefficients multiply by changes in or erodible width is equal to the width of the active the flow velocity value from one cross-section to the Lisar River flow, used for the hydraulic study of the next cross-section to calculate the energy loss between river. Fall velocity of fine sediment was another spe- them. The U.S. Army Corps of Engineers defined cific value for the simulation. Developed formulas to a range of the Cc and Ce values according to the estimate this value have differences in the number of changes in river environmental variables. Field obser- fine particles and their adhesion and colloidal natures. vations indicate the gradual changes along the mining- Therefore, we suggest the Ruby relations for determin- affected reach of the Lisar River. Therefore, Cc and Ce ing the fall velocity of sediment considering the spe- coefficient values were 0.1 and 0.3, respectively, based cific size and weight of the particles and the kinematic on the form of the convergence and divergence of the viscosity coefficient of the fluid. river channel. According to Lorang and Aggett’s Exponential function (Figure 4) shows a significant (2005) manual, flow energy loss is the function of correlation between the sediment capacity – roughness that is an indicator of the water level and a dependent variable – and the average flow velocity, flow velocity at each considered section. shear stress, and flow rate as an independent variable. According to the mentioned relations, with an increase in the flow discharge, the sedimentation capa- 2.6. Info on sediments, bedload, and river flow city also increases and vice versa. It means that river flow has a remarkable effect on changing the morphol- The Lisar River sediment discharge relations analyses ogy of the river. using the sediment rating curve (Figure 4), FAO, and co-concentration cap methods. Evaluations showed that the sediment rating curve method is appropriate, 2.7. Hydrological characteristics and its values (see Figure 4) were used as sediment boundary conditions upstream of the river (Figure 4). The Lisar River hydrological specifications in the form Finally, the long-term sediment transport data of the of the observed daily discharges on average were 6 S. A. HOSSEINI ET AL. Figure 4. Relationship between flow rate and sediment rating curve of Siahjafar hydrometric station. introduced into the model. Classification of the resul- sediment transport simulation is sensitive to the tant data for each defined category for modeling (a choice of sediment transport function, and the quasi-unsteady simulation mode) is carried out Manning roughness coefficient manifested in the rate (Table 2). Further, the continuous flow hydrograph of erosion and sedimentation in a cross-section. As is expressed as a discrete hydrograph. The flow values a result, different conditions were considered to cali- in each time interval are assumed to be steady flow. To brate the model sediment. Since there are various define the hydraulic boundaries condition for model- sediment estimation functions in the HEC_RAS ing, the flow hydrograph (see Table 2) for the model, it is necessary to compare the results of each upstream section and the water depth in normal status of these functions with the results of sediments mea- for the downstream section were applied. sured at the site of the SiahJafar station to determine In addition, seasonal water temperature informa- the closest answer to the sediment estimation. tion is defined for the simulation. Therefore, consider- ing temperature values in modeling, the kinematic viscosity coefficient for river water produced and the 3. Results and discussion fall velocity of the sedimentation rate were obtained. 3.1. The hydraulic variables obtained for the Lisar River 2.8. Model calibration The simulation of the Lisar River flow presents the hydraulic parameters (Table 3), including the maxi- The Manning roughness coefficient is a fundamental mum, minimum, and average values of the depth of parameter in hydraulic modeling in various river engi- water, stream power, flow area, width surface, flow neering studies that control the simulation output velocity, shear stress, energy slope, wetted perimeter, data. Therefore, the calibration procedure of the and Froude Number. Experiences showed (Javaheri, hydraulic model was performed using a series of this 2014) that an increase in the shear stress proliferates parameter before the input of sediment data. The Table 2. Classification of daily discharge at the Siahjafar station. 3 −1 No. Flow (m s ) Flow duration (h) Computation increments (h) 1 0.54 68,136 40 2 1.25 67,584 30 3 1.95 54,024 30 4 3.59 43,752 30 5 6.46 8040 15 6 8.58 3264 15 7 11.33 2880 15 8 13.68 744 15 9 16.33 696 15 10 20.38 168 15 11 24.07 144 15 12 27.88 120 8 13 34.25 96 8 14 52.95 48 4 15 82.00 24 2 GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Table 3. The brief output of hydraulic calculations in the peak discharge of Lisar River at Siahjafar station. Width Froude Hydraulic Energy Flow Wetted Flow Stream Shear Parameter surface number depth slope area perimeter velocity power stress 2 2 limits (m) - (m) (m/m) (m ) (m) (m/s) (N/ms) (N/m ) Maximum 286.7 1.0 0.9 0.037 130.3 287.1 2.4 397.4 164.2 Minimum 46.6 0.3 0.3 0.002 33.9 46.7 0.6 7.5 11.9 Average 130.7 0.8 0.5 0.019 56.1 130.8 1.6 149.8 80.9 the stream power, which influences the rate of erosion m, equal to 0.162 million tons considering the bulk and sedimentation in a river (see Table 3). density of riverbed materials. As a result, the Variation in flow parameters includes Froude num- annual sediment transport based on the Siahjafar ber, velocity, and shear stress at different sections station is about 0.0528 million tons. Seemingly, this along with the 4 km representative range of the Lisar value is more than three times the allowable River illustrated in Figures 5–7. Results showed that amount of excavation from the Lisar River. the parameters do not change along the path and are According to the simulation of long-term (29 almost uniform. years) data of the Siahjafar hydrology station, the volume of sediment discharge of the Lisar River is about 4 million tons per year on average. It should 3.2. The sediment load in total be noted that the simulations have presented the The running of the seven sediment transfer func- sediment discharge from each given cross-section tions (Yang; Ackers–White; Engelund–Hansen; and the whole of the sediment discharge computed Copeland’s form of Laursen; Meyer, Peter, and from the last cross-section. Muller; Toffaleti; and Wilcock–Crowe) under the According to recommended values by Vanoni quasi-unsteady flow condition provided the total (2013) for bedload rate, the portion of bed load sedi- sediment load. Accordingly, the model showed ment was proposed to be about 20% of the suspended that using each function brought out a variety of load based on the slope and physiography of the sediment conveyance capacities. The sediment load watershed, geological conditions of the basin, and of the Siahjfar hydrometric station is about engineering judgment. As a result, the Lisar River 0.044 million tons per year which resulted from bed load is about 0.009 million tons per year, and the the exponential relation between sediment loads total amount of sediment load (Qst) is about and the daily water discharge (see Figure 4). 0.0528 million tons per year. According to the resultant data, the Yang function The application of the Yang function also has been provided the sediment values with the highest simi- approved by Hosseini et al. (2012) who performed larity (see Table 4) with the observational data simulations based on classified data (37 years) from (Siahjafar station) at the lowest error (0.24). Kharoud River discharge. The HEC-RAS model cali- Therefore, the total sediment load is brated based on water and sediment discharges 0.0397 million tons per year based on the Yang showed the Yang sediment transport equation with function. According to surveying operations results, a 27% error was the closest relation to the sediment field observations, and distribution of the mining data of the regional hydrometric station. However, 3 −1 sites, the excavation volumes are 90,000 m year Asadi et al. (2017) study that had similar Figure 5. Maximum rate of change in Froude number along the path. 8 S. A. HOSSEINI ET AL. Figure 6. Maximum rate of change in velocity along the path. Figure 7. Maximum rate of change in shear stress along the path. Figure 8. Topographical changes in the longitudinal profile of the Lisar river because of mining. environmental conditions to the study area showed Qolgachi, ; Akbari & Faghfur Maghrebi, 2011; that among the sediment transport functions of the Akbarzadeh et al., 2011; Imamqolizadeh et al., 2009) HEC-RAS model, the Meyer–Peter–Muller function is to highlight the capability of considered model and the most consistent with reality and can be used to environmental characteristics. For instance, similar predict section changes in the Talar River. results in sediment transport in the Karoun River The present research results were compared (Table. (Iran) with similar sediment texture using the HEC- 5) with the previous works (Ahmadian & Naseri RAS model were found by Akbarzadeh et al. (2011) in GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Table 4. Quantitative comparison of the results obtained from the implementation of the model and the measured values of the Lisar River. Mass capacity Percentage of error compared No. Transport function (million ton/year) to the measured sediment 1 Sediment rating curve 0.0521 0.00 2 Ackers–White 0.000001 1.00 3 Engelund–Hansen 0.2255 −3.33 4 Laursen 0.3750 −6.20 5 Meyer–Peter–Muler 0.0092 0.82 6 Toffaletti 0.0178 0.66 7 Yang 0.0397 0.24 8 Wilcock 0.0051 0.90 Table 5. Comparison of the resultant data with the previous works in the model function and environmental features. References Appropriate function Sediment texture Channel morphology Bed slope % Affected by mining The present study Yang Sandy-gravel Straight 2.1 Yes Akbarzadeh et al. (2011) Yang Sandy-gravel Meandering 0.9 No Ahmadian and Naseri Qolgachi () Laursen Sand Meandering 0.7 No Imamqolizadeh et al. (2009) Laursen Sand Meandering 0.4 No Asadi et al. (2018) Meyer–Peter–Muller Sand-gravel Straight 1 Yes which the Yang function simulated the most fitted Honarbakhsh et al. (2020) work on changes in the result compared with observational data. The present riverbed elevation. They revealed the effects of riv- research found that the sediment transport functions erine mining on the changes in bed elevation along are not very sensitive to calculating the particle fall rate the Farsan River (Iran). They showed scouring pro- in modeling the riverbed shape change. Akbari and cess at a rate between 0.05 m and 4 m along the Faghfur Maghrebi (2011) and the present research extracted zone was responsible for elevation agree that the differences in the results depend on changes. Further, their simulations showed the selection of the sediment transport equation. a 0.12% reduction in the volume of sediment out- However, in simulations on sediment transport in put from the last given cross-section after the the Aji-Chai River (Ahmadian & Naseri Qolgachi, mining operation. Such a condition is comparable 2010) and Talar River (Imamqolizadeh et al., 2009) with an 0.26% increase in shear stress. in which the sandy texture was the dominant particle size of sediment and rivers have a lower slope, the best performance belonged to the Larsen function. 3.4. The appropriate areas for mining regarding Consequently, the Yang function recommends use erosion/sedimentation trend in simulations of the rivers with sandy-gravel texture Variation in the sedimentation rate along the repre- accompanied with or without riverine mining and sentative range of the Lisar River is illustrated in steep slope or mountainous area where the entire Figure 10. Following the ascending route of the given shear stress of the substrate focuses on the coarser curve, the Lisar River is affected by severe erosion particles. from the beginning to about 340 m, pointing to a sensitivity to material excavation. As a result, the present study suggests that this range is suitable for 3.3. Changes in elevation of the riverbed other uses like water-intake structures or assembling Topographical changes in the river bed based on a pumping station. The river range between 1000 and the long-term data were another output of the 1300 m from the beginning (Figure 10) suggested for model simulations. Surveying operations revealed developing intersection structures such as bridges. that the maximum and average riverbed elevation In contrast, the ranges along the river (350–760 m; changes along the studied range of the Lisar River 220–2370 m; 3300–3600 m) contributed to the have occurred at 3 and 1.2 m (Figure 8), respec- enhancement in the curve slope representing an tively. Further, there are severe changes between increase in deposition instead of sediment transport, 260 and 350 m from the beginning of the studied suggesting the places for mining. cross-section, where mining operations have dis- The present study revealed that the most destruc- turbed the river channel significantly (Figure 9). tive impact of riverine mining along the Lisar River is In contrast, the lowest topographical change with bed and bank erosion and vivid morphological 0.11 m is observed from a cross-section from changes. Mining cause changes in normal channel 2600 m of the beginning of the study range. The movement and the sedimentary regime in which the present study’s results compared with the rivers prefer to deposit the sediment inside the bends. 10 S. A. HOSSEINI ET AL. Figure 9. Spatial distribution of topographical changes in the bed river along the studied cross-sections. At the same time, erosion will happen at the river bank Simulations described the spatial pattern of erosion on the outer side. Accordingly, the inner side of the and deposition along the studied ranges of the Lisar river bends of the river is a suitable place for the River. According to the sediment transport simula- excavation of materials. Dredging extra materials tion (see section 2.8), the exact allowable depth of the from the river bed can improve the river and increase sand mining is 1 m. This depth is suggested based on sediment conveyance capacity. Sand extraction causes calculated sediment volume in each cross-section and the suspension of bed sediments and adverse ecologi- operational conditions at existing mines. As a result, cal effects on the river’s aquatic life. the suitable mining sites should locate in ranges Therefore, the mining plans in such a sensitive where the annual sedimentation rate is 20 cm, in environment should follow an executive permitted which deposition can compensate for sand extrac- range and volume according to the detailed river sedi- tion. Furthermore, considering the width of the ment processes analysis. Further, to predict the char- river and its buffer zone, the exact spatial places for acteristics of the flow in a section of the river, there is riverine mining are determined. In order to optimize a need for boundary conditions consistent with the model simulation, evidence obtained through nature. field observations and surveyed maps used. Riverbank erosion protection strategies are firmly required to stabilize the Lisar River banks regarding 3.5. The allowed depth of mining for the Lisar excavation. According to Kondolf et al. (2002), River applying management strategies can adapt the Determining the exact spatial distribution of allowed response of the river channel to extraction, and mining was another aim of the present research. often repeated engineering interventions are Figure 10. The trend of deposition of materials along the studied range of the Lisar River. GEOLOGY, ECOLOGY, AND LANDSCAPES 11 required. Simulations on sediment and the statistical Funding analysis demonstrated that the locations designated The work was supported by the Soil Conservation and in the study for excavation are comparable with 63% Watershed Management Research Institute [970866-035- of the places recommended by engineering 29-29-0]. judgments. ORCID 4. Conclusions Seyed Ahmad Hosseini http://orcid.org/0000-0002- The present research has proved an integrated approach 0235-7017 using the HEC-RAS model, and field surveying coin- cided with simulation and estimates of the sediment regime of a river undermining operation. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Jan 5, 2023
Keywords: HEC-RAS; gravel mining; river; sedimentation; simulation