Simulating the Impact of Climate Change with Different Reservoir

water

Article Simulating the Impact of Climate Change with Diﬀerent Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

Muhammad Adnan Khan 1,* , Jürgen Stamm 1 and Sajjad Haider 2

1 Institute of Hydraulic Engineering and Technical Hydromechanics, Technische Universität Dresden, 01062 Dresden, Germany; [email protected] 2 NUST Institute of Civil Engineering (NICE), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; [email protected] * Correspondence: [email protected]; Tel.: +49-176-68671333

Received: 10 August 2020; Accepted: 28 September 2020; Published: 30 September 2020

Abstract: Reservoir sedimentation reduces the gross storage capacity of dams and also negatively impacts turbine functioning, posing a danger to turbine inlets. When the sediment delta approaches the dam, further concerns arise regarding sediments passing through turbine intakes, blades abrasion due to increased silt/sand concentration, choking of outlets, and dam safety. Thus, slowing down the delta advance rate is a worthy goal from a dam manager’s viewpoint. These problems can be solved through a flexible reservoir operation strategy that prioritize sediment deposition further away from the dam face. As a case study, the Mangla Reservoir in Pakistan is selected to elaborate the operational strategy. The methodology rests upon usage of a 1D sediment transport model to quantify the impact of different reservoir operating strategies on sedimentation. Further, in order to assess the long-term effect of a changing climate, a global climate model under representative concentration pathways scenarios 4.5 and 8.5 for the 21st century is used. The reduction of uncertainty in the suspended sediments concentration is achieved by employing an artificial neural networking technique. Moreover, a sensitivity analysis focused on estimating the impact of various parameters on sediment transport modelling was conducted. The results show that a gradual increase in the reservoir minimum operating level slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of a sustainable reservoir use for the societal benefit.

Keywords: sedimentation; sediment delta; reservoir operation; changing climate; artiﬁcial neural networking; sustainable reservoir

1. Introduction Climate change has impacted the environment in many ways, and today, considerable attention is being paid to the hydrological impact of climate change, and more speciﬁcally, the evaluation of the potential impacts of climate change on future water availability and extreme events [1,2]. An important corollary of this focus is the review of reservoir operational strategies and water distribution policies under climate change scenarios. In this regard, an integrated approach will be used to propose the best doable actions and policies to cope with potential climate changes. Reservoirs plays an important role in fulﬁlling the water demands of a growing world population. However, its impoundment leads to delta formation due to reduced sediment transport capacity and

Water 2020, 12, 2736; doi:10.3390/w12102736 www.mdpi.com/journal/water Water 2020, 12, 2736 2 of 27 faster deposition rate [3]. The result is the depletion of the reservoir storage volume. According to an estimate by White [4], worldwide gross storage capacity of reservoirs has declined by about one percent annually due to sedimentation. Besides, sediment deposition in narrow channels increases the probability of flooding in the riparian areas upstream of the reservoir. Additionally, water released from a reservoir exerts a key influence on the downstream river morphology, sediment balance, and changes in the ecological properties of the river. It has been found that the reservoir storage loss due to sediment deposition is one of the key impediments in sustainable use of water resources at existing or in future reservoir management [5]. The useful life span of a reservoir is generally less than 100 years, but in some cases, it filled up much earlier than its designed life [6]. For instance, the well-known Sanmenxia Dam on the Yellow River in China faced severe deposition. It had to be reconstructed to provide an additional outlet to increase the sediment flushing capacity [5]. Similarly, the Tarbela Reservoir in Pakistan lost about one third of its storage capacity over 40 years, and the Warsak Reservoir in Pakistan almost filled with sediments after just 30 years of operation [7]. The Girba Reservoir in Sudan lost more than 53% of its storage capacity in 40 years of operation due to sedimentation [8]. There are a number of sediment removal techniques, e.g., dry excavation, hydraulic dredging, sluicing, and siphoning [4]. Flushing is a technique, in which the sediment-laden flows are passed through a reservoir downstream during the flood season, as well as re-suspending and removing the already deposited sediment [9]. However, all these possible solutions consume large proportions of water that will not be available for the intended purposes of the reservoir; similarly, drawing down the reservoir level during operation may cause the problems to shift to other regions. Further ways to enhance reservoir life by managing the incoming sediment load outside of the reservoir include the decrease of incoming sediment load either by modified watershed management, upstream sediment trapping, controlling soil erosion, or bypassing the turbid flows [10]. Thus, reservoir sedimentation reduces the useful life of a dam and leads to a failure to discharge the intended functions for which the dam was built in the first place, i.e., provision of irrigation water, municipal water supply, production of hydropower, and navigation [11]. Further, the sediment deposition may cause damage to the dam structure by choking the outlets and threatening the dam’s safety [12,13]. Reservoir operation significantly affects the reservoir sediment dynamics. Normally the maximum proportion of sediment load enters the reservoir during the high flow events. Most of the incoming sediments are deposited in a reservoir during such events if a high reservoir level is maintained. On the other hand, if the reservoir water level has been kept low, a large quantity of incoming sediments travels through the reservoir and often leads to degradation of already deposited sediments [14,15]. Rehman (2018) [14] and Petkovsek (2014) [15] estimated that sediment delta advancement is reduced by increasing the minimum reservoir operating level, but it also decreases the storage volume of the reservoir and vice versa. It is therefore clear that the reservoir operation policy has a critical influence on the life span of the reservoir. Generally, numerical models have been used to analyze and quantify the impact of different measures adopted for sediment management. Normally, 1D models were often applied to simulate long-term sedimentation because of its computational efficiency in comparison to 2D/3D models [16]. The 1D models can give better performance if the reservoir has a broader width, as well as varying lateral forces of hydraulic and morphodynamics are not dominant [17]. However, 1D models cannot predict bed levels accurately on the channel bends and near to the hydraulic structures where the helical flow is formed due to the secondary currents, which can only be simulated by 3D flow and sediment models [18]. In this study, a well-known sediment model, HEC-RAS [19], developed by the Hydrological Engineering Center, USA, is used to simulate the long-term sedimentation trend in the reservoir. This paper mainly focuses on different operational strategies for reservoirs to manage sediments. As a case study, we have selected the Mangla Reservoir in northern Pakistan. A few studies have Water 2020, 12, 2736 3 of 27 been performed in the Jhelum River Basin (JRB) and its tributaries regarding the change in future precipitation and flows under the impact of climate change [20–22]. Further, only three studies have been carried out regarding incoming suspended sediment yield into the Mangla Reservoir [1,23,24]. So far, no studies have been conducted to find the potential impacts of climate change with different reservoir operating strategies on sedimentation of the Mangla Reservoir. The sedimentation in the Mangla Reservoir is a big challenge for its management, similar to any other large reservoirs. The chief objective of the Mangla Reservoir is to supply irrigation water to an extensive network of canals in the Punjab province, the so-called food basket of Pakistan. In order to fulfill this objective, the flows of the Jhelum River and its tributaries are stored in the reservoir up to the conservation level during the high flow period. Moreover, water is released through the reservoir down to the minimal operating level to fulfill the downstream irrigation water demand. When the incoming flows are high and the reservoir level is low, then previously settled sediments are reworked and are moved towards the dam outlets [25]. However, this is not without pitfalls, as it might affect the power production, which is a secondary but a valuable objective for the power-starved economy of Pakistan. Thus, the operating level stands out as a key parameter that critically influences the reservoir’s sustainability and the yield from irrigation flows and hydroelectricity from the reservoir. Clearly, the operational policy should be such as to maximize the yield without jeopardizing the longevity of the reservoir. The objective of the present study is to explore the risk to the Mangla Dam’s future reservoir operations under a changing climate by devising a set of plausible scenarios (operation rules) and examining its effect by using 1D modeling. In this research, several basic theoretical operational rules were developed and analyzed to assess the reservoir sedimentation in a generalized way. These rules should be of value to dams having no special flushing outlets for sediments, as numerous reservoirs commissioned during 1960s to 1970s had no such facilities. To analyze the future sedimentation in the reservoir, projected flows that derived from the Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 (projected rise in mean temperature 1.10–2.60 ◦C under RCP4.5 and 2.60–4.80 ◦C under RCP8.5) for 21st century were used [26]. Similarly, missing and future suspended sediments predicted by the artificial neural networking technique were used as the boundary conditions in the 1D sediment model to calculate the bed level changes and sediment delta advancement rate with greater precision. The outcome of this study will help to improve the reservoir operation rules related to reservoir sedimentation and ultimately support the mission of a sustainable reservoir use for societal benefit.

2. Study Area

2.1. Description of the Study Area Mangla Dam is a multifunction dam situated on Jhelum River in Mirpur, Azad Kashmir, Pakistan. The dam was constructed in 1967 and is located about 108 km south-east from Pakistan’s capital, Islamabad. The total catchment area of the Jhelum River Basin (JRB) up to the dam site is 33,397 km2, while the river length is about 725 km. The dam is geographically located at 33◦000 N–35◦120 N and longitudes of 73◦070 E–75◦400 E (Figure1). A majority of the catchment area consists of hills and rugged mountains with elevations varying from 272 m above sea level (a.s.l.) to 6290 m a.s.l. The northern part of the basin is covered by snowcapped mountains. About 45% of the watershed areas of JRB is found in Pakistan administered Kashmir, while the remaining 55% lies in Indian-administered Kashmir [27]. The slope of the JRB watershed is distributed into three ranges: undulating lands: 0–3%, steep slopes: 8–30%, and mountainous landL more than 30% [21]. A major part of the watershed consists of mountainous land and steep slopes. Water 2020, 12, x FOR PEER REVIEW 4 of 28

Kanshi, Poonch, Khad, and Jari are the minor tributaries. The Surface Water Hydrology (SWH) Department has installed gauging stations to measure the daily runoff and sediment loads at different sites of the Jhelum, Poonch, and Kanshi Rivers at Kohala and Azad Pattan, Kotli, and Palote, respectively. The dam was constructed to store the surplus water of JRB and, primarily, supply the water for irrigation during the low flow period i.e., October–March. Its secondary purpose is to generate hydroelectricity from the outflows and also acts to control the floods during the months of monsoon (July– September) [28]. Its benefits to Pakistan’s agriculture-based economy are immense, as it irrigates 6

Watermillion2020 hectare, 12, 2736 area of land and contributes about 6% of the total power production of the country4 of 27 with an installed capacity of 1000 MW [28].

Figure 1. The Mangla Reservoir catchment area.

The JhelumMangla RiverReservoir emerges has a from total the initial Verinag gross Spring storage that capacity lies amid (GSC) the of Pir 7.25 Panjal billion and cubic Himalayan meters mountain(BCM) at the ranges normal in the conservation state of Jammu level (NCL) and Kashmir. of 366.35 Two m a.s.l., main while tributaries it has ofa dead the Jhelum storage River capacity are theof 0.66 Kunhar BCM and at the Neelum previous Rivers minimum that confluence operating the level main (MOL) stream of at Kohala316.99 m Bridge a.s.l. andBetween Muza theffarabad, years respectively.1967–2010, the The reservoir Mangla lost Reservoir storage capacity is fed by equal two to perennial 1.59 BCM rivers (21.93%) (Jhelum owing and to reservoir Poonch) siltation. and one non-perennialTo enhance the river impounding (Kanshi). Thecapacity Jhelum and River raise is thethe majorhydropower tributary potential of the Mangla of Mangla Reservoir, dam, anda dam the Kanshi,raising Project Poonch, in Khad,2012 was and undertaken, Jari are the which minor led tributaries. to an increase The in Surface dam height Water by Hydrology 9 m (from (SWH)366.35 Departmentm a.s.l. to 378.543 has installed m a.s.l.). gauging Thus, the stations net enhanc to measureed storage the daily capacity runoff ofand the sediment upraised loads Mangla at di Damfferent is sites3.451 of BCM the Jhelum, with a Poonch,GSC of 9.11 and BCM Kanshi at Rivers the updated at Kohala conservation and Azad Pattan, level. The Kotli, additional and Palote, 3.451 respectively. BCM of waterThe that dam is greater was constructed than the lost to storage store the capacity surplus during water 1967–2010 of JRB and, will primarily, also produce supply an additional the water for644 irrigationmillion energy during units the lowannually flow periodand irrigate i.e., October–March. 0.53 million hectares Its secondary of land. purpose Moreover, is to the generate raised hydro-electricityMangla Dam would from reduce the outflows the flood and alsomenace acts toby control decreasing the floods the magnitude during the monthsof flood of as monsoon well as (July–September)fulfilling the increasing [28]. Its irrigation benefits to demand. Pakistan’s Reservoir agriculture-based levels and economy corresponding are immense, GSC are as itshown irrigates in 6Figure million 2. hectare area of land and contributes about 6% of the total power production of the country withThe an installed total inflow capacity of Mangla of 1000 Reservoir MW [28]. is coming through three main rivers i.e., Jhelum, Poonch, and KanshiThe Mangla and their Reservoir contribution has a total to total initial annual gross inflow storage of reservoir capacity (GSC)is 83%, of 16%, 7.25 and billion 1% cubicrespectively. meters (BCM) at the normal conservation level (NCL) of 366.35 m a.s.l., while it has a dead storage capacity of 0.66 BCM at the previous minimum operating level (MOL) of 316.99 m a.s.l. Between the years 1967–2010, the reservoir lost storage capacity equal to 1.59 BCM (21.93%) owing to reservoir siltation. To enhance the impounding capacity and raise the hydropower potential of Mangla dam, a dam raising Project in 2012 was undertaken, which led to an increase in dam height by 9 m (from 366.35 m a.s.l. to 378.543 m a.s.l.). Thus, the net enhanced storage capacity of the upraised Mangla Dam is 3.451 BCM with a GSC of 9.11 BCM at the updated conservation level. The additional 3.451 BCM of water that is greater than the lost storage capacity during 1967–2010 will also produce an additional 644 million energy units annually and irrigate 0.53 million hectares of land. Moreover, the raised Mangla Dam Water 2020, 12, x FOR PEER REVIEW 5 of 28

The mean annual runoff from 1979–2017 of the Jhelum River at Azad Pattan station is 833 m3/s, Poonch River at Kotli Station is 133 m3/s and Kanshi River at Palote station is 6.5 m3/s (Figure 1). Seventy five percent of the total inflow in Mangla reservoir arrives during the March to August period, mostly due to monsoon rainfall with a small snowmelt component. Thus, river runoff starts toWater increase2020, 12, 2736from March–April onward, and peak flows are observed in May–July due5of to 27 superimposed of snowmelt and monsoon rainfall (June–August) [21]. The mean monthly discharge of the Jhelum River varies between 309 m3/s and 1929 m3/s at the Mangla Dam. The Jhelum River is knownwould reduceto be thea flood-prone flood menace river, by decreasing and the theSept magnitudeember 2014 of floodflood aswas well ex astremely fulfilling severe, the increasing with a maximumirrigation demand.flow of 17,950 Reservoir m3/s levelsrecorded and at corresponding Mangla [29]. GSC are shown in Figure2.

Figure 2. Stage-storageStage-storage capacity capacity curve curve for for the the Mangla Mangla Reservoir. Reservoir.

AThe perusal total inflowof the ofdata Mangla from 1979 Reservoir to 2010 is comingshowed throughthat the threeprecipitation main rivers is spatially i.e., Jhelum, varying Poonch, over theand Mangla Kanshi watershed and their contribution and heavy precipitation to total annual occurred inflow ofin reservoirnorthern ispart 83%, of the 16%, basin. and 1%Whereas respectively. in the meanThe mean annual annual precipitation runoff from in the 1979–2017 southern and of thenorthern Jhelum part River of the at Mangla Azad Pattan Basin is station 846 mm is 833and m18933/s, mm,Poonch respectively. River at Kotli About Station 1196 is 133mm m avg.3/s and annual Kanshi prec Riveripitation at Palote from station 1961–2012 is 6.5 mis3 /observeds (Figure 1in). the ManglaSeventy Basin. five Almost percent 50% of theof totalthe precipitation inflow in Mangla occurs reservoir in the arrivesnorthern during part theof the March basin to Augustduring December–Marchperiod, mostly due in to the monsoon form of rainfall snow with[21]. aApart small from snowmelt the rain component. and snowfall, Thus, glaciers river runo areff anotherstarts to sourceincrease of from river March–Aprilflow. The average onward, annual and minimu peak flowsm and are maximum observed intemperatures May–July due of the to superimposedMangla Basin areof snowmelt 0.3 °C in December–January and monsoon rainfall and (June–August) 29.5 °C in June–July, [21]. The respectively mean monthly [1]. discharge of the Jhelum River varies between 309 m3/s and 1929 m3/s at the Mangla Dam. The Jhelum River is known to be 2.2.a flood-prone Sources of Sediment river, and in thethe Jhelum September River 2014 Basin flood was extremely severe, with a maximum flow of 17,950 m3/s recorded at Mangla [29]. The catchment area of the Jhelum, Poonch, Khad, and Kanshi Rivers above Mangla being A perusal of the data from 1979 to 2010 showed that the precipitation is spatially varying over mountainous is subjected to weathering due to severe climatic condition. The major causes of the Mangla watershed and heavy precipitation occurred in northern part of the basin. Whereas in sediment mobilization are deforestation, developments in the area, earthquakes, and heavy rainfall, the mean annual precipitation in the southern and northern part of the Mangla Basin is 846 mm and which ultimately finds its way into the rivers [1,24]. 1893 mm, respectively. About 1196 mm avg. annual precipitation from 1961–2012 is observed in Since 1967 to 2012, the Mangla Reservoir lost 22.15% of its GSC at a conservation level of 366.36 the Mangla Basin. Almost 50% of the precipitation occurs in the northern part of the basin during m a.s.l. due to the natural phenomenon of reservoir siltation. In the period after the dam raising in December–March in the form of snow [21]. Apart from the rain and snowfall, glaciers are another 2012, it lost 15.45% of total capacity until 2017, with reference to the conservation level 378.56 m a.s.l. source of river flow. The average annual minimum and maximum temperatures of the Mangla Basin The Mangla Reservoir has been losing its capacity at the rate of 0.31% per annum. The average are 0.3 C in December–January and 29.5 C in June–July, respectively [1]. sediment◦ deposition during 1967–2017 was◦ 0.03 BCM per annum, and the total sediment deposition was2.2. Sources1.67 BCM. of Sediment Reservoir in the sedimentation Jhelum River Basin not only leads to a dwindled impounding capacity of reservoir but also affects the long-term reservoir operational pattern. SinceThe catchment 1960, the Water area of& thePower Jhelum, Development Poonch, Auth Khad,ority, and Pakistan Kanshi (WAPDA) Rivers above has Manglabeen running being differentmountainous watershed is subjected management to weathering programs due in the to severeJhelum climatic River Basin condition. to slow The down major the causessediment of inflowsediment into mobilization the channels are and deforestation, to control soil developments erosion. Numerous in the tasks area, have earthquakes, been performed and heavy to improve rainfall, which ultimately finds its way into the rivers [1,24].

Since 1967 to 2012, the Mangla Reservoir lost 22.15% of its GSC at a conservation level of 366.36 m a.s.l. due to the natural phenomenon of reservoir siltation. In the period after the dam raising in 2012, it lost 15.45% of total capacity until 2017, with reference to the conservation level 378.56 m a.s.l. The Mangla Reservoir has been losing its capacity at the rate of 0.31% per annum. The average Water 2020, 12, 2736 6 of 27 sediment deposition during 1967–2017 was 0.03 BCM per annum, and the total sediment deposition was 1.67 BCM. Reservoir sedimentation not only leads to a dwindled impounding capacity of reservoir but also affects the long-term reservoir operational pattern. Since 1960, the Water & Power Development Authority, Pakistan (WAPDA) has been running different watershed management programs in the Jhelum River Basin to slow down the sediment inflow into the channels and to control soil erosion. Numerous tasks have been performed to improve the Mangla watershed by planting more than 133 million trees along with the construction of earthen and stone structures measuring 3.45 Mm3 in the form of retaining walls and check dams, and improving cultivated land of about 34 km2, with provision of special training to farmers [30]. Consequently,the sediment load has declined to 33.6 MCM/annum from 51.8 MCM/annum, as predicted by the Mangla Dam Consultant during 1950–1960.

2.3. Impact of Sediments on Turbines As sediment delta moves toward the dam, more silt and sand particles are carried with the flow that pass through the turbines. During high flood events, the sediment quantity can dramatically increase. This carries the risk of abrasion caused to turbine blades as well as the choking of the turbine intake tunnels. In 1992, an exceptionally high flood peak of 30,865 m3/s passed through the reservoir, which triggered the deposited sediment movement towards the dam. In this flood event, both the main spillway and the power tunnels were operational. The measured sediment concentrations passing through the outlet structures were in excess of 10% by weight for over 100 h, with peak concentrations being three times that value [31]. Although the turbines remained unscathed, the cooling system was choked with sediment and subsequently had to be shut-off for cleaning. Further, this event prompted the authorities to close the power tunnels during future high floods. The advancing delta could increase the risk of foreset slumping under earthquake loading as well as due to the effect of changes in operating rules. Thus, the delta movement has serious consequences for a safe and efficient functioning of the dam as well as on its stability. The delta movement is dependent on the sediment deposition trend and on the dam regulation.

3. Data Description

3.1. Reservoir Geometric Data The geometric data describes the physical characteristics of the shape and size of the river and reservoir. The Mangla Reservoir has a complex geometry and is divided into six pockets, namely, the main Mangla (MM), lower Jhelum (LJ), upper Jhelum (UJ), Poonch, Kanshi, and Khad pockets (Figure3). The Jhelum River is the main source of the Mangla Reservoir, which is considered as the main stem composed of upper Jhelum, lower Jhelum, and main Mangla pockets. A total of 136 cross sections measured by the hydrographic survey division of WAPDA, for the year 2005 were used to build the reservoir geometry in the numerical model. The distribution of the number of cross sections for diﬀerent pockets shows that 88 cross sections are for the main stem of Jhelum River, 17 for Kanshi pocket, 16 for the Poonch pocket, and 15 for the Khad and Jari pocket. A schematic diagram showing the Jhelum River and its tributaries along with the location of cross sections representing river geometry in the HEC-RAS model is presented in Figure3. Water 2020, 12, 2736 7 of 27 Water 2020, 12, x FOR PEER REVIEW 7 of 28

FigureFigure 3. 3.ManglaMangla Reservoir Reservoir pockets andand cross cross sections sections location. location. 3.2. River Flow Data 3.2. River Flow Data Daily gauged river flow data from different gauging stations of WAPDA were used to set up the flowDaily and gauged sediment river transport flow data model from in different HEC-RAS. gaug Theing river stations gauging of stations WAPDA for were the Jhelum, used to Kanshi, set up the flowand and Poonch sediment Rivers transport are located model at Azad in HEC-RAS. Pattan, Palote, Th ande river at Kotli, gauging respectively. stations The for flowthe Jhelum, data related Kanshi, and toPoonch years 2005–2017Rivers are was located used at for Azad the modeling. Pattan, Palote, and at Kotli, respectively. The flow data related to years 2005–2017To envisage was theimpact used for of climatethe modeling. change on sedimentation at the Mangla Dam, future projected hydrologicalTo envisage datathe impact (2018–2100) of climate of all change the tributaries on sedimentation of the Mangla at the Reservoir Mangla underDam, RCP4.5future projected and hydrologicalRCP8.5 by data Naeem (2018–2100) et al. [22 of,32 all] werethe tributarie used ins the of the numerical Mangla model Reservoir as the under upstream RCP4.5 boundary and RCP8.5 by Naeemcondition. et al.Naeem [22,32] et al.were[22 ,32used] conducted in the numeri a studycal on model the Mangla as the watershedupstream andboundary explored condition. the impacts of climate change on Mangla Dam hydrology using the soil and water assessment tool Naeem et al. [22,32] conducted a study on the Mangla watershed and explored the impacts of climate (SWAT). For assessing the climate change impacts on flow regimes for three future periods (2011–2040, change on Mangla Dam hydrology using the soil and water assessment tool (SWAT). For assessing 2041–2070, and 2071–2100), three Global Circulation Models (GCMs) (CanESM2, BCC-CSM1-1, the climateand MICROC5), change impacts and two on representative flow regimes concentration for three future pathways periods (RCP4.5 (2011–2040, andRCP8.5) 2041–2070, with and two 2071– 2100),downscaling three Global methods Circulation (Statistical Models Downscaling (GCMs) (CanESM2, Model (SDSM) BCC-CSM1-1, and Long Ashton and MICROC5), Research Station and two representativeWeather Generator concentration (LARS-WG)). pathways According (RCP4.5 to his statistical and RCP8.5) analysis results,with two BCC-CSM1-1 downscaling with SDSM methods (Statisticalperformed Downscaling better as compared Model to(SDSM) other models and Long for the Ashton Mangla DamResearch watershed. Station Under Weather RCP4.5 Generator and (LARS-WG)).RCP8.5, percent According increase to inhis observed statistical discharges analysis is results, shown inBCC-CSM1-1 Figure4. with SDSM performed better as compared to other models for the Mangla Dam watershed. Under RCP4.5 and RCP8.5, percent 3.3. Sediment Data increase in observed discharges is shown in Figure 4. The sediment yield can be estimated by correlating observed sediment load and water discharge at the time of measurement and applying the same relation to the daily flows of a year, thereby estimating the sediment load at different gauging stations of Mangla reservoir. In 1979, the Surface Water Hydrology Project (SWHP) of WAPDA started discharge measurements along with suspended sediment concentration (SSC) at the Azad Pattan gauging station of Jhelum River, Kotli gauging station of Poonch River from 1960, and similarly at Palote station of Kanshi River. Since then, daily

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river flow data and occasional SSC measurements at the gauging stations were recorded to ultimately estimate the sediment inflow into the Mangla Reservoir. Furthermore, to account for the unmeasured bed load, the practice followed by the dam designers has been to take it as equal to 10% of the suspended sediment load (SS) and adding it to the latter to get the total sediment load for the Mangla Reservoir [33,34]. The SSC data is recorded in units of parts per million (ppm). The breakup of the SSC data Figure 4. Percent changes in annual runoff. expresses the percentage of its three constituents i.e., sand, silt, and clay. The average percentage of clay, silt and sand in the SS at Azad Pattan gauging station is 26%, 62%, and 12%; that at Kotli gauging 3.3. Sediment Data station is 27%, 65%, and 8%; and at Palote gauging station is 32%, 58%, and 10%, respectively. Figure5 presentsThe sediment the SSC yield for the can Jhelum be estimated River at theby Azadcorrelati Pattanng observed gauging station sediment of Jhelum load Riverand water for diff dischargeerent at theflows. time From of measurement the pit sample, and the applying riverbed gradation the same curve relation for di tofferent the daily Mangla flows Reservoir of a pocketsyear, thereby is estimatingWater shown2020, 12 the in, x FigureFOR sediment PEER6. REVIEW load at different gauging stations of Mangla reservoir. In 1979, the Surface8 of 28 Water Hydrology Project (SWHP) of WAPDA started discharge measurements along with suspended sediment concentration (SSC) at the Azad Pattan gauging station of Jhelum River, Kotli gauging station of Poonch River from 1960, and similarly at Palote station of Kanshi River. Since then, daily river flow data and occasional SSC measurements at the gauging stations were recorded to ultimately estimate the sediment inflow into the Mangla Reservoir. Furthermore, to account for the unmeasured bed load, the practice followed by the dam designers has been to take it as equal to 10% of the suspended sediment load (SS) and adding it to the latter to get the total sediment load for the Mangla Reservoir [33,34]. The SSC data is recorded in units of parts per million (ppm). The breakup of the SSC data expresses the percentage of its three constituents i.e., sand, silt, and clay. The average percentage of clay, silt and sand in the SS at Azad Pattan gauging station is 26%, 62%, and 12%; that at Kotli gauging station is 27%, 65%, and 8%; and at Palote gauging station is 32%, 58%, and 10%, respectively. Figure 5 presents the SSC for the Jhelum River at the Azad Pattan gauging station of Jhelum River for different flows. From the pit sample, the riverbed gradation curve for different Mangla Reservoir FigureFigure 4. 4. PercentPercent changes changes in annualannual runo runoff.ff. pockets is shown in Figure 6. 3.3. Sediment Data The sediment yield can be estimated by correlating observed sediment load and water discharge at the time of measurement and applying the same relation to the daily flows of a year, thereby estimating the sediment load at different gauging stations of Mangla reservoir. In 1979, the Surface Water Hydrology Project (SWHP) of WAPDA started discharge measurements along with suspended sediment concentration (SSC) at the Azad Pattan gauging station of Jhelum River, Kotli gauging station of Poonch River from 1960, and similarly at Palote station of Kanshi River. Since then, daily river flow data and occasional SSC measurements at the gauging stations were recorded to ultimately estimate the sediment inflow into the Mangla Reservoir. Furthermore, to account for the unmeasured bed load, the practice followed by the dam designers has been to take it as equal to 10% of the suspended sediment load (SS) and adding it to the latter to get the total sediment load for the Mangla Reservoir [33,34]. The SSC data is recorded in units of parts per million (ppm). The breakup of the SSC data expresses the percentage of its three constituents i.e., sand, silt, and clay. The average percentage of clay, silt and sand inFigure theFigure SS 5. at Percentage 5. AzadPercentage Pattan variation variation gauging of of sand, sand, station silt and andis 26%, clay clay for 62%,for Jhelum Jhelum and River. 12%; River. that at Kotli gauging station is 27%, 65%, and 8%; and at Palote gauging station is 32%, 58%, and 10%, respectively. Figure 5 presents the SSC for the Jhelum River at the Azad Pattan gauging station of Jhelum River for different flows. From the pit sample, the riverbed gradation curve for different Mangla Reservoir pockets is shown in Figure 6.

Figure 5. Percentage variation of sand, silt and clay for Jhelum River.

Water 2020, 12, 2736 9 of 27 Water 2020, 12, x FOR PEER REVIEW 9 of 28

FigureFigure 6. 6.BedBed gradation gradation curve curve of of didifferentﬀerent Mangla Mangla pockets. pockets.

The Thedata data related related to sediment to sediment concentrations concentrations al alongong withwith waterwater discharges discharges are are collected collected according according to United States Geological Survey (USGS) specifications for random days throughout the year at to United States Geological Survey (USGS) specifications for random days throughout the year at controlled section of the river [35–37]. Therefore, to predict the missing sediment data, researchers controlled section of the river [35–37]. Therefore, to predict the missing sediment data, researchers employed different methods i.e., the conventional sediment rating curves (SRCs) method [38] and the employedartificial different neural networking methods i.e., (ANN) the techniqueconventional [39]. sediment rating curves (SRCs) method [38] and the artificialIt has neural been observednetworking in several (ANN) reservoir technique sedimentation [39]. studies that missing and future sediment inflowsIt has been predicted observed by SRCs in several are often reservoir in error sediment by as muchation as 50% studies [40,41 that]. missing and future sediment inflows predictedIn the current by SRCs study, are both often methods in error were by applied as much to predict as 50% the [40,41]. missing sediment load, and then statisticalIn the current analysis study, were both employed methods to determine were applied the best to estimationpredict the technique. missing sediment Thereafter, load, predicted and then statisticaldata fromanalysis leading were technique employed was to useddetermine in numerical the best modeling estimation to technique. calibrate and Thereafter, perform futurepredicted data analysisfrom leading estimation. technique was used in numerical modeling to calibrate and perform future analysis estimation.3.4. Reservoir Operational Levels

3.4. ReservoirDaily Operational reservoir level Levels data measured by the Mangla Dam Organization (MDO) of WAPDA for the duration of 2005–2017 (18 years) was employed as a downstream boundary condition to calibrate the numericalDaily reservoir model. level For thedata future measured prediction by the after Mangla year 2017, Dam the Organization reservoir levels (MDO) have been of WAPDA derived for the durationthrough variousof 2005–2017 management (18 years) scenarios was employed of the reservoir as a operationaldownstream rules. boundary condition to calibrate the numericalSeasonal model. variations For the in future river prediction flows along after with year demands 2017, the for reservoir irrigation, levels hydroelectric have been power derived throughgeneration, various and management flood control scenarios reservoir of operation the reservoir combine operational to create a reservoirrules. operating rules curve forSeasonal filling andvariations drawing in down river the flows reservoir along [42]. with demands for irrigation, hydroelectric power generation,Incoming and flood water control discharges reservoir and operation reservoir operation combine (water to create level a reservoir and outflow) operating both aff rulesect the curve for fillingreservoir and sediment drawing dynamics. down the As reservoir the future [42]. pool levels are not known at the dam, various scenarios have to be developed and simulated to establish the uncertainties inherent in predictions of future Incoming water discharges and reservoir operation (water level and outflow) both affect the trends. It is surmised that the future reservoir operation levels have a greater impact on reservoir reservoir sediment dynamics. As the future pool levels are not known at the dam, various scenarios sedimentation than the water inflow time series [14,15]. have to beThe developed reservoir operationaland simulated levels to atestablish the dam the site uncertainties follow an annual inherent fill and in predictions drawdown cycle:of future trends.the It water is surmised level starts that to the increase future in reservoir April, reaches operation a maximum levels duringhave a the greater months impact of August on reservoir and sedimentationSeptember, than and thenthe water drop,attaining inflow time a minimum series [14,15]. level in the month of March (Figure7). The reservoir operational levels at the dam site follow an annual fill and drawdown cycle: the water level starts to increase in April, reaches a maximum during the months of August and September, and then drop, attaining a minimum level in the month of March (Figure 7).

Water 2020, 12, 2736 10 of 27 Water 20202020,, 1212,, xx FORFOR PEERPEER REVIEWREVIEW 1010 ofof 2828

FigureFigure 7. DailyDaily mean mean reservoir reservoir inflow/outflow inﬂow/outﬂow and leve levelll before before and and after after the ManglaMangla upraising.upraising.

Generalized reservoir-operating rule rule curves have been proposed to analyze the reservoir operational impact on reservoir sedimentation sedimentation and and its its movement (Figure8 8).8).). TheseTheseThese curvescurvescurves representrepresentrepresent thethe minimum minimum and and maximum maximum level level along along with with their their duration. The The curves curves depict depict a a linear linear behavior, behavior, which indicates the filling ﬁlling of reservoir and and fall fall and and corresponds corresponds to to maximum maximum outflows, outﬂows, respectively. respectively. This approach isis followedfollowed inin engineeringengineering studiesstudies thatthat treattreat reservoirreservoir sedimentationsedimentation [[14,15,43].14,15,43].

Figure 8. FutureFuture reservoir operation curves.

This paper analyzes five five feasible scenarios (S1– (S1–S5)S5) with the change in minimum operation level (MOL)(MOL) forforfor up-to up-toup-to year yearyear 2065. 2065.2065. For ForFor the thethe first firstfirst (S1), (S1),(S1), second secondsecond (S2), (S2),(S2), third thirdthird (S3), (S3),(S3), and andand fourth fourthfourth scenarios scenariosscenarios (S4), the(S4),(S4), MOL thethe wasMOL kept was at kept 320 mat a.s.l.,320 m 323 a.s.l., m a.s.l., 323 m 326 a.s.l., m a.s.l., 326 andm a.s.l., 329 mand a.s.l., 329 respectively, m a.s.l., respectively, for the next for 58 the years next from 58 yearsyear 2017 from onward, year 2017 as shown onward, in Figure as shown8. Further, in Figure the fifth 8. Further, scenario the (S5), fifth envisaged scenario an (S5), increase envisaged of 0.75 an m increaseinincrease MOL ofof every0.750.75 mm year inin MOLMOL starting ofof everyevery from yearyear 320 startingstarting m a.s.l. tofromfrom 329 320320 m a.s.l.mm a.s.l.a.s.l. for toto next 329329 12 mm year,a.s.l.a.s.l. forfor then nextnext MOL 1212 year,year, remained thenthen MOLfixed atremained 329 m a.s.l. fixed for at the 329 remainder m a.s.l. for of the the remainder duration up of tothethe the durationduration year 2065. upup toto In thethe practical yearyear 2065.2065. terms, InIn the practicalpractical above terms,strategiesterms, thethe areaboveabove a measure strategiesstrategies to reduceareare aa measuremeasure the rate toto of reducereduce sediment thethe deltaraterate ofof movement sedimentsediment delta towardsdelta movementmovement the dam towardstowards face. thethe dam Theface. aforementioned reservoir operating scenarios are formulated based on the past pool level fluctuationsThe aforementioned in a wet and dry reservoir year depending operating upon scenarios the observed are formulated irrigation based demand on the since past its pool upraising level fluctuationsandfluctuations Mangla inin Reservoir aa wetwet andand operating drydry yearyear rule dependingdepending curves. uponupon thethe observedobserved irrigationirrigation demanddemand sincesince itsits upraisingupraising and Mangla Reservoir operating rule curves.

Water 2020, 12, 2736 11 of 27

4. Methodology

4.1. Statistical Measures for Model Performance Evaluation The goodness-of-fit measures applied to assess various simulations showing different selections of parameters are Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), and percent bias (PBIAS) [44,45]. The NSE exhibits how good the observed plot fits the simulated plot and R2 defines the degree of collinearity between observed and simulated data, i.e., the proportion of the variance. Both NSE and R2 lies between 0–1, with higher values showing less error, and values larger than 0.5 are normally considered acceptable. RMSE is a standard method to calculate the error of a model in forecasting quantitative data. PBIAS estimate the mean tendency of the computed data to be greater or lesser than observed. The optimal value of PBIAS is 0, while values falling in between 15% are considered acceptable. Positive and negative values indicate the model ± overestimation and underestimation, respectively. The above-cited parameters were calculated by the following equations: Pn 2 = (Oi Si) NSE = 1 i 1 − , (1) − Pn 2 O Oavg i=1 i −  2  Pn   = Oi Oavg Si Savg  2  i 1 − −  R =  q q  , (2)  Pn 2 Pn 2   O Oavg S Savg  i=1 i − i=1 i − s 2 Xn (Si Oi) RMSE = − , (3) i=1 n Pn ! i=1(Si Oi) PBIAS = 100 Pn − , (4) × i=1 Oi where S is the simulated value, O is the observed value at each cross section, n is the number of observed data entries, and avg is the average of the total values.

4.2. Missing and Future Sediment Prediction Techniques

4.2.1. Sediment Rating Curves (SRCs) SRCs for different gauging stations have been derived from recorded data. Thus, the daily suspended sediment load was calculated through SRCs. The daily-suspended sediment load has been expressed in power law form as a function of daily flows [38,46] at different gauging stations, as represented by following equations.

2 1.9638 For Jhelum River, Qs = (7.7 10− ) Q (5) × × 1.8748 For Poonch River, Qs = 0.208 Q (6) × 9 2.9873 For Kanshi River, Qs = (1.159 10− ) Q (7) × × 3 where Qs is the daily-suspended sediment load (Tons/day) and Q is the daily avg. river ﬂow (m /s).

4.2.2. Artificial Neural Network Model (ANN) Today, artificial intelligence (AI)-based techniques are being used more and more for problem solving in hydrology. A number of researchers predicted the hydrological parameter by developing artificial neural network (ANN)-based models [47,48]. For many years, ANN-based models have been used for rainfall estimation, runoff estimation, reservoir inflow prediction, suspended sediment prediction, reservoir level estimation, and reservoir operation [39,49–52]. In this study, missing and Water 2020, 12, 2736 12 of 27 future suspended sediment data were predicted by ANN-based data-driven technique for the efficient estimation of incoming suspended sediment. The adopted technique has been developed using different input parameters i.e., antecedent river discharges, sediment, precipitation, and temperature information. For the development of a smooth and reliable model, state of the art mathematical tools—gamma test and M-test—were used for the estimation of the best model input combination and data length selection, respectively [53,54]. Model training was performed through two types of feed forward ANN model that are formed on back propagation (BP) and a two-layer Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm. Assessment for the best technique was done based on simple statistical parameters, namely NSE, R2, and PBIAS. Outcomes from both training and validation stages clearly indicated the efficiency of both the conventional SRCs method and ANN-based prediction technique for missing and future sediment data. Results showed that the ANN-based model outperformed the SRCs method with higher values of NSE, R2. Further, PBIAS shows that SRCs significantly underestimate the sediment load and ANN gave best estimation of suspended sediment load (Table1).

Table 1. Statistical analysis of the performance of the sediment ratings curve (SRC) and artiﬁcial neural network (ANN) models in predicting the daily suspended sediment load.

Method NSE R2 PBIAS SRCs 0.29 0.49 45.72% − ANN 0.78 0.78 +1.37%

4.3. Hydraulic Model Description The evaluation of reservoir life entails modelling the spatial sediment deposition with given water and sediment discharges and channel geometry as inputs and driven by the reservoir operation policy that determines the reservoir levels. This step requires the realization of comprehensive numerical simulations to cover the full extent of reservoir operations under varying conditions of water and sediment flow. In this study, the numerical model HEC-RAS 5.0.7 is run to predict the future sedimentation patterns in the Mangla Reservoir. It is a one-dimensional sediment transport model that predicts reservoir sedimentation trends from moderate to long-term period and loss of reservoir impounding capacity along with other exhaustive info, like bed composition, sediment concentrations, etc., at specific locations. HEC-RAS can perform four types of hydraulic analysis, i.e., steady-flow analysis for water surface profile, one- and two-dimensional unsteady flow simulations, one-dimensional sediment transport computation, and water quality analysis modelling [19]. To compute the flow depth and velocity, a flow profile equation is used to estimate water depth, water level, and mean velocity variation within two consecutive sections in the reservoir for a given computation time interval. The backward standard step method is applied for computing the water surface profile. Further, the Manning equation is used to calculate the flow rate within each section [55]. The Exner equation (Equation (7)), also known as the sediment continuity equation, is used in the HEC-RAS model for sediment routing in each section between two consecutive cross sections along the river reach. ∂η ∂Qs 1 λρ B = , (8) − ∂t − ∂x 3 where Qs, sediment load (m /s); B, width of channel (m); λρ active layer porosity; η, channel bed level elevation (m); t, Time (s); x, distance (m). The Exner equation relates the net sediment entering the control volume during a small time step dt to the change in the channel bed level. In the field, the control volume is replaced by a given river reach and the aggradation or degradation of the channel bed is due to the imbalance between the sediment entering and the sediment leaving the reach. This highlights the importance of sediment transport capacity of a river reach, which is determined via uniform flow sediment transport formulae/functions. Water 2020, 12, 2736 13 of 27

The HEC-RAS model have eight sediment transport functions Ackers and White (A&W) (1973) [56], Laursen (1958) [57], Englund–Hansen (E&H) (1967) [58], Meyer-Peter and Müller (1948) [59], Copeland (1989) [60], Yang (1973, 1984) [61,62], Toﬀaleti (1968) [63], and Wilcock and Crowe (2003) [64] to calculate the sediment transport capacity. Among all the functions, the Ackers and White sediment transport formula was found to be best by sensitivity analysis and it was also used in studies conducted on JRB [16,65]. It takes account of parameters of transport, particle size, and mobility. It has a dimensionless parameter for distinguishing between ﬁne sediment (less than 0.04 mm), transitionary particles (0.04–2.5 mm), and coarse sediments (greater than 2.5 mm). The general sediment transport rate is determined in terms of some dimensionless parameters by Equation (8) in the following form.

 1 n 1 − " !# 2   n ys −  V  Fgr = U gd 1   , (9) y  q  ∗ −  αD  32 log d where Fgr, mobility number for sediment; U , shear velocity (m/s); n, Transition component, depending ∗ on sediment size; α, coefficient equal to 10 in rough turbulent equation; V, mean flow velocity (m/s); d, size of sediment particles (mm); and D, depth of water (m). Bed volume changes owing to the finer particles, e.g., clay and silt are estimated by using other transport functions to account for cohesive characteristics. HEC-RAS offers the possibility of using the standard transport equations or the Krone (1962) [66] and Partheniades (1965) [67] approach. A comprehensive explanation of the model is available in HEC-RAS reference manuals [19]. To compute the bed sorting and armoring, HEC-RAS uses the Thomas mixing method (formerly Exner 5 and 7) that computes the coarse armor or inactive layer, which limits the transport capacity of a river [19]. This method defines the gradations of the active layer, which comprises the cover and subsurface layers, in order to estimate the transport capacity of a river.

5. Results and Discussion

5.1. Sensitivity Analysis for Calibration and Validation of the Numerical Model A river system model was created in the HEC-RAS according to Figure3. It includes Jhelum River and its tributaries. The river x-sections helped to create the bathymetry and bank geometry. Water and sediment discharges (divided into sediment fractions of sand, silt and clay) on daily basis were applied to each river at its upper most x-section as upstream boundary conditions. The dam face formed the model end section on the downstream side where the daily reservoir level in a.s.l. was applied as the downstream boundary condition. Sensitivity analysis for the selection of certain parameters like roughness coefficients, computational time step, sediment transport functions, and fall velocity was performed to simulate bathymetric changes in the reservoir. After importing all the input data to the HEC-RAS model, the model was run for different Manning’s values going from 0.02 to 0.04 with the incremented by 0.005 and by keeping a computational time step of 12 h with the model default sediment parameters (the Ackers and White method as the sediment transport function, Exner 5 as the bed sorting method, and Report 12 as the fall velocity method). It is found that low values of n lead to high erosion and cause high sediment transport rates because of the higher flow velocities. The simulated bed profile for Manning’s value of 0.03 was found to agree closely with the observed bed level (Figure9), and statistical analysis also showed that the value of n = 0.03 is the most suitable one because it has a minimum RMSE value and NSE, R2 value is close to 1 as shown in Table2. Thus, n = 0.03 was adopted as the project default value. Further, it has also been observed that the increasing value of n from 0.02 to 0.03 leads to the computed bed profile closer to the measured profile, and its further increased value (n = 0.035) shows more deviation from the observed. Thus, it is observed that a nonlinear relation exists between reservoir sedimentation and the Manning’s roughness coefficient. Water 2020, 12, 2736 14 of 27 Water 2020, 12, x FOR PEER REVIEW 14 of 28

Figure 9. 9. ComparisonComparison of the of observed the observed and simulated and simulated bed elevation bed elevation for different for Manning’s diﬀerent Manning’sroughness coefficients.roughness coe ﬃcients.

Further, toTable analyze 2. Statistical the effect analysis of time for sensitivitystep, the model analysis was for calibrationrun for the and computational validation. increment as 24, 12, 9, 6, and 3 h, by keeping other parameters same as default. It is observed that the computed Calibration 2010 Validation 2014 results are sensitive to the varying computational increment. The smaller time step gives more Parameter RMSE RMSE stability to the numerical model, but itNSE increases R2 the PBIASrun time. Therefore,NSE it is R an2 importantPBIAS model parameter that directly influences the model’s efficiency. The(m) comparison of the observed(m) and computed results for then 0.02year 2010 and0.95 2014 are 0.96 shown0.29 in Figure2.83 10, 0.88and from 0.92 statistical0.17 analysis3.41 − − (Table 2), it is observed nthat 0.025 similar results0.96 were 0.96 found 0.22for the computational2.65 0.88 increment 0.92 0.13 equal or3.39 less than 6 h. Hence,cient in the present study, 6 h is selected− as computational increment time− step as the

ﬃ n 0.03 0.96 0.97 0.20 2.55 0.89 0.92 0.10 3.34 project default value for further analysis. − − coe roughness

Manning’s n 0.035 0.96 0.97 0.20 2.59 0.89 0.92 0.11 3.36 − − n 0.04 0.96 0.97 0.20 2.57 0.89 0.92 0.11 3.35 − − 3 h 0.96 0.97 0.20 2.54 0.89 0.92 0.07 3.25 − − 6 h 0.96 0.97 0.20 2.54 0.89 0.92 0.07 3.25 − − 9 h 0.96 0.97 0.20 2.55 0.89 0.92 0.09 3.29 − −

increment 12 h 0.96 0.97 0.21 2.55 0.89 0.92 0.10 3.34

Computational − − 24 h 0.96 0.97 0.21 2.56 0.89 0.92 0.02 3.38 − − A&W 0.96 0.97 0.20 2.54 0.89 0.92 0.07 3.25 − − E&H 0.93 0.95 0.52 3.40 0.87 0.92 0.42 3.57 − − Laursen 0.96 0.97 0.21 2.54 0.89 0.92 0.09 3.26 − − MPM 0.91 0.96 0.13 3.93 0.56 0.94 0.62 6.57 function transport Sediment Report 12 Yang 0.95 0.96 0.27 2.80 0.89 0.92 0.15 3.28 − − Figure 10. ComparisonToﬀaleti of observed0.96 and simulated 0.97 0.18bed elevation2.62 for 0.85 different 0.95 computational 0.08 3.84 increments. − Report-12 0.96 0.97 0.20 2.54 0.89 0.92 0.07 3.25 − − Furthermore, Rubeythe model was calibrated0.96 on 0.97 observ0.20ed longitudinal2.55 0.89 bed profile 0.92 data0.09 and3.29 delta − − advance rate, fromvan 2005 Rijn to 2010, by selecting0.96 0.97one by one0.20 all the2.52 sediment 0.90 transport 0.93 functions0.06 3.18 and

A&W − − different fallmethod velocityDietrich methods available0.96 in HEC-RAS 0.97 0.20software2.54 by keeping 0.89 the 0.92 6-h computational0.09 3.29 Fall velocity − − increment time andTo ffthealeti Manning’s roughness0.96 0.97coefficient0.20 value2.54 as 0.03. 0.89 The results 0.92 showed0.09 that3.28 the Ackers and White sediment transport function combined− with the Van Rijn fall velocity− method was found to be the best among the lot. All other transport functions underestimated the location of the Further, to analyze the effect of time step, the model was run for the computational increment as delta pivot point, but overall, a good agreement was found between the observed and simulated 24, 12, 9, 6, and 3 h, by keeping other parameters same as default. It is observed that the computed longitudinal bed profiles, as shown in Figure 11. The original ground level (OGL) of the reservoir in results are sensitive to the varying computational increment. The smaller time step gives more stability 1967 is also shown in Figure 11. to the numerical model, but it increases the run time. Therefore, it is an important model parameter that directly influences the model’s efficiency. The comparison of the observed and computed results

Water 2020, 12, x FOR PEER REVIEW 14 of 28

Figure 9. Comparison of the observed and simulated bed elevation for different Manning’s roughness coefficients.

Further, to analyze the effect of time step, the model was run for the computational increment as 24, 12, 9, 6, and 3 h, by keeping other parameters same as default. It is observed that the computed resultsWater 2020 are, 12 ,sensitive 2736 to the varying computational increment. The smaller time step gives 15more of 27 stability to the numerical model, but it increases the run time. Therefore, it is an important model parameter that directly influences the model’s efficiency. The comparison of the observed and computedfor the year results 2010 and for 2014the year are shown 2010 and in Figure 2014 10are, andshown from in statistical Figure 10, analysis and from (Table statistical2), it is observed analysis (Tablethat similar 2), it is results observed were that found similar for theresults computational were found increment for the computational equal or less increment than 6 h. Hence, equal or in less the thanpresent 6 h. study, Hence, 6 hin is the selected present as study, computational 6 h is sele incrementcted as computational time step as the increment project defaulttime step value as the for projectfurther default analysis. value for further analysis.

Figure 10.10.Comparison Comparison of observedof observed and simulatedand simulated bed elevation bed elevation for different for computationaldifferent computational increments. increments. Furthermore, the model was calibrated on observed longitudinal bed profile data and delta advanceFurthermore, rate, from the 2005 model to 2010, was by calibrated selecting on one observ by oneed alllongitudinal the sediment bed transport profile data functions and delta and advancedifferent rate, fall velocityfrom 2005 methods to 2010, available by selecting in HEC-RAS one by one software all the bysediment keeping transport the 6-h computationalfunctions and differentincrement fall time velocity and the methods Manning’s available roughness in HEC-RAS coefficient software value as by 0.03. keeping The resultsthe 6-h showed computational that the Ackersincrement and time White and sediment the Manning’s transport roughness function coeffi combinedcient withvalue the as Van0.03. Rijn The fallresults velocity showed method that wasthe Ackersfound toand be White the best sediment among thetransport lot. All function other transport combined functions with the underestimated Van Rijn fall velocity the location method of was the founddeltapivot to be point,the best but among overall, thea lot. good All agreementother transport was foundfunctions between underestimated the observed the andlocation simulated of the deltalongitudinal pivot point, bed profiles, but overall, as shown a good in Figureagreem 11ent. The was original found groundbetween level the (OGL)observed of theand reservoir simulated in Water1967longitudinal 2020 is also, 12, shownx bed FOR profiles,PEER in Figure REVIEW as 11shown . in Figure 11. The original ground level (OGL) of the reservoir15 of 28in 1967 is also shown in Figure 11.

FigureFigure 11. 11. ComparisonComparison of observed of observed and simulated and simulated bed elevation bed elevation for different for sediment diﬀerent transport sediment functions.transport functions.

AllAll functions underestimated underestimated the the deposition deposition close close to to the the dam dam face; the earthquakes of of 2005 2005 and and 20082008 could be the reason behind th thee movement of the foreset slope. The The result result of of the calibration in 20102010 is shown in Figure 12.12.

Figure 12. Calibration of the numerical model for the year 2010.

Model Validation The numerical model was validated for the years 2014 and 2017 by comparing the computed and observed longitudinal bed profiles of the reservoir, as illustrated in Figure 13. From Figure 13, it is revealed that the topset slope closely matches the topset slope of the observed delta profile. A fairly good agreement was found between the simulated and observed measurement of the bed profile exclusively on the far and near of the topset slope and on the pivot point of the sediment delta. However, minor discrepancies have been observed on locations where the river channel is narrow and on the river bends. This is due to the presence of 3D effects, i.e., helical flow due to secondary currents, which can only be resolved by a 3D flow and sediment model [18]. Further, the model underestimated the sediment deposition close to the dam embankment. Overall, the results of the calibration and validation of the numerical model indicate that the model was able to compute the bed levels with reasonable accuracy, thus pointing to its suitability for the reservoir sedimentation studies.

Water 2020, 12, x FOR PEER REVIEW 15 of 28

Figure 11. Comparison of observed and simulated bed elevation for different sediment transport functions.

All functions underestimated the deposition close to the dam face; the earthquakes of 2005 and Water2008 2020could, 12 ,be 2736 the reason behind the movement of the foreset slope. The result of the calibration16 of in 27 2010 is shown in Figure 12.

Figure 12. Calibration of the numerical model for the year 2010.

Model Validation The numerical model was validated for the ye yearsars 2014 and 2017 by comparing the computed and observed longitudinal longitudinal bed bed profiles profiles of of the the reservoi reservoir,r, as as illustrated illustrated in in Figure Figure 13. 13 From. From Figure Figure 13, 13 it, isit revealed is revealed that that the thetopset topset slope slope closely closely matches matches the topset the topsetslope of slope the observed of the observed delta profile. delta A profile. fairly Agood fairly agreement good agreement was found was between found between the simulated the simulated and observed and observed measurement measurement of the bed of theprofile bed profileexclusively exclusively on the onfar the and far near and nearof the of topset the topset slope slope and and on onthe the pivot pivot point point of of the the sediment sediment delta. delta. However, minorminor discrepanciesdiscrepancies have have been been observed observed on on locations locations where where the the river river channel channel is narrow is narrow and andon the on river the river bends. bends. This isThis due is to due the to presence the presence of 3D eofffects, 3D effects, i.e., helical i.e., flowhelical due flow to secondary due to secondary currents, whichcurrents, can which only be can resolved only be by resolved a 3D flow by and a 3D sediment flow and model sediment [18]. Further, model the [18]. model Further, underestimated the model underestimatedthe sediment deposition the sediment close deposition to the dam close embankment. to the dam Overall, embankment. the results Overall, of the the calibration results of andthe validationcalibration ofand the validation numerical of model the numerical indicate thatmodel the indicate model wasthat ablethe model to compute was able the bedto compute levels with the bedreasonableWater levels 2020, 12 with accuracy,, x FOR reasonable PEER thus REVIEW pointing accuracy, to itsthus suitability pointing for to theits suitability reservoir sedimentation for the reservoir studies. sedimentation16 of 28 studies.

Figure 13. ValidationValidation of the numerical model for the year 2014.

Table 2. Statistical analysis for sensitivity analysis for calibration and validation.

Calibration 2010 Validation 2014 Parameter RMSE RMSE NSE R2 PBIAS NSE R2 PBIAS (m) (m) n 0.02 0.95 0.96 −0.29 2.83 0.88 0.92 −0.17 3.41 n 0.025 0.96 0.96 −0.22 2.65 0.88 0.92 −0.13 3.39 n 0.03 0.96 0.97 −0.20 2.55 0.89 0.92 −0.10 3.34 n 0.035 0.96 0.97 −0.20 2.59 0.89 0.92 −0.11 3.36 roughness roughness coefficient coefficient Manning’s Manning’s n 0.04 0.96 0.97 −0.20 2.57 0.89 0.92 −0.11 3.35 3 h 0.96 0.97 −0.20 2.54 0.89 0.92 −0.07 3.25 6 h 0.96 0.97 −0.20 2.54 0.89 0.92 −0.07 3.25 9 h 0.96 0.97 −0.20 2.55 0.89 0.92 −0.09 3.29 12 h 0.96 0.97 −0.21 2.55 0.89 0.92 −0.10 3.34 increment increment

Computational 24 h 0.96 0.97 −0.21 2.56 0.89 0.92 −0.02 3.38 A&W 0.96 0.97 −0.20 2.54 0.89 0.92 −0.07 3.25 E&H 0.93 0.95 −0.52 3.40 0.87 0.92 −0.42 3.57 Laursen 0.96 0.97 −0.21 2.54 0.89 0.92 −0.09 3.26 MPM 0.91 0.96 0.13 3.93 0.56 0.94 0.62 6.57 Report 12 Sediment Yang 0.95 0.96 −0.27 2.80 0.89 0.92 −0.15 3.28

transport function function transport Toffaleti 0.96 0.97 −0.18 2.62 0.85 0.95 0.08 3.84 Report-12 0.96 0.97 −0.20 2.54 0.89 0.92 −0.07 3.25 Rubey 0.96 0.97 −0.20 2.55 0.89 0.92 −0.09 3.29 van Rijn 0.96 0.97 −0.20 2.52 0.90 0.93 −0.06 3.18 A&W A&W

method method Dietrich 0.96 0.97 −0.20 2.54 0.89 0.92 −0.09 3.29

Fall velocity velocity Fall Toffaleti 0.96 0.97 −0.20 2.54 0.89 0.92 −0.09 3.28

The statistical analysis was performed to evaluate the calibration and validation of sediment transport model efficiency. The results show that the model performed well in both phases of calibration and validation by considering the combination of the Ackers and White equation as the transport function, the van Rijn equation as the fall velocity method, a Manning’s roughness coefficient of 0.03, and a computational increment of 6 h. The minimum RMSE (calibration 2.18 and validation 3.18) with NSE (0.96, 0.90), R2 (0.97, 0.93), and PBIAS is close to the perfect values of 1 and 0%, respectively, as shown in Table 2.

Water 2020, 12, 2736 17 of 27

The statistical analysis was performed to evaluate the calibration and validation of sediment transport model efficiency. The results show that the model performed well in both phases of calibration and validation by considering the combination of the Ackers and White equation as the transport function, the van Rijn equation as the fall velocity method, a Manning’s roughness coefficient of 0.03, and a computational increment of 6 h. The minimum RMSE (calibration 2.18 and validation 3.18) with NSE (0.96, 0.90), R2 (0.97, 0.93), and PBIAS is close to the perfect values of 1 and 0%, respectively, as shown in Table2. The model efficiency was also assessed by comparing the sediment deposition in different pockets of the Mangla Reservoir as given in Table3. It has been observed that the computed volume of deposited sediments in different reservoir pockets are quite close to the observed amount as reported by the dam authorities.

Table 3. Sediment deposition in diﬀerent pocket of reservoir from 2005 to 2010.

Reservoir Pocket Observed Deposition (MCM) Computed Deposition (MCM) Upper Jhelum 49.46 56.76 Lower Jhelum 2.31 13.33 Main 54.16 51.77

At the majority of the x-section locations, a good agreement between the observed and simulated bed levels was noted, although the results were poor in the lower Jhelum River reach. This is because the said reach is sandwiched between the upper Jhelum and the lower Jhelum reach. It is a short river reach, which connects the two reaches transversally. The channel width is narrow and marked by the presence of bends where a complex 2D or even 3D flow prevails, hence, the wide gap between the observed and simulated sediment results in Table3. However, bed levels near the dam are not well simulated by the numerical model. In the period 2005–2017, the bed levels simulated with HEC-RAS do not follow the increase shown in the surveys (Figures 12 and 13). One probable explanation could be the devastating earthquakes of October 2005, 2008, 2011, 2013, 2014, and 2015 (having magnitudes of 7.6, 6.4, 7.2, 7.7, 4.5, and 6.3, respectively) in northern Pakistan, which may have caused landslide/subsidence of the foreset slope of the sediment delta. It is clear that such phenomena cannot be modeled by HEC-RAS. In Figure 13, it can be seen that, in the year 2014, the bed levels near to the dam embankment are higher than the tunnel intake levels. The fact that the tunnels still function and have not been choked by the higher flow velocity is the eroding of the sediment deposits. These localized phenomena, of course, cannot be replicated by a 1D model, as they occur due to lateral variation of the flow velocity, which requires at least a 2D model for its description.

5.2. Impact of Future Reservoir Operating Scenarios The reservoir operation depends upon a number of factors, i.e., inflows and its seasonal variation, downstream water level, MOL, sediment movement pattern and rate, flow rate change due to variable climate, trap efficiency, live and dead storage, etc. Clearly, determining the optimum reservoir operation strategy is by no means evident. Thus, we adopted a heuristic approach that allows us to select the best strategy and at the same time explains the trade-offs between the main factors by developing a set of plausible operating scenarios as listed in Table4. Water 2020, 12, 2736 18 of 27

Table 4. Model boundary condition for future simulation.

Downstream Sediment Fall Upstream Boundary Manning’s Scenarios Boundary Transport Velocity Condition Roughness Condition Function Method Daily Q predicted by RCP 4.5 and 8.5. Future reservoir Temperature (RCP 4.5 Ackers and Scenario 1 operation level of S1 0.03 Van Rijn and 8.5). Daily SSL White (MOL 320 m a.s.l.) predicted by RCP 4.5 and 8.5 (2017–2075) Scenario 2 - (MOL 323 m a.s.l.) - - - Scenario 3 - (MOL 326 m a.s.l.) - - - Scenario 4 - (MOL 329 m a.s.l.) - - - (MOL 320–329 m Scenario 5 - a.s.l.) by an increase --- of 0.75 m/year

The results for Scenario S1 with projected future discharges from RCP4.5 and the simulation outcomes for the years 2025, 2035, 2045, and 2055 are shown in Figure 14. As the future projections for the Mangla Basin predict an increase in ﬂows, Scenario 1, which keeps the MOL equal to the existing 319 m a.s.l., leads to rapid sedimentation. As a result, the delta moves rapidly toward the dam face. In this scenario, the bed levels close to the main embankment aggraded rapidly and reached the level

Water320 m 2020 a.s.l., 12,in x FOR the PEER year REVIEW 2045, thus, rapidly ﬁlling the dead storage space. 19 of 28

Figure 14. BedBed profile proﬁle of the deposited sediment for S1-45 scenario.

Figure 15 represents the pivot point location and the bed proﬁles after 30 years for the ﬁve reservoir operation scenarios.

Figure 15. The pivot point of the deposited sediment delta of the year 2035 for all operational scenarios.

This shows that the raise in MOL slows down the rate of delta movement towards the turbine intakes as more and more sediments are deposited along the topset slope and in the upstream river reaches. Furthermore, it is clearly observed that by increasing the minimum reservoir level, the live storage capacity decreases gradually and the dead storage remains intact. In Scenarios S3 and S4, the movement of the delta reduced significantly as the high water level slowed down the velocity in areas far upstream from the dam, thus promoting deposition at those locations. Table 5 shows the mean advancement rate of the Mangla delta toward the dam face.

Table 5. Delta advance rate towards the dam face.

Scenarios S1-4.5 S2-4.5 S3-4.5 S4-4.5 S5-4.5 Delta advancement rate (km/year) 0.183 0.169 0.150 0.113 0.131

Table 6 shows the total volume lost as percentage of gross storage capacity (including the upraised Mangla Dam capacity) for the analyzed scenarios using future discharges derived from RCP4.5 and 8.5, which varies from 30.98% to 32.75%. The results reveal that there is a maximum difference of 1.77% in the total storage lost among different operational scenarios. It also shows the impact between the two sets of future discharges (RCP4.5 and 8.5) on reservoir sedimentation up to

Water 2020, 12, x FOR PEER REVIEW 19 of 28

Water 2020, 12, 2736 19 of 27 Figure 14. Bed profile of the deposited sediment for S1-45 scenario.

Figure 15.15. TheThepivot pivot point point of theof the deposited deposited sediment sediment delta delta of the of year the 2035 year for 2035 all operational for all operational scenarios. scenarios. This shows that the raise in MOL slows down the rate of delta movement towards the turbine intakesThis as shows more andthat morethe raise sediments in MOL are slows deposited down alongthe rate the of topset delta slopemovement and in towards the upstream the turbine river intakesreaches. as Furthermore, more and more it is sediments clearly observed are deposited that by al increasingong the topset the minimum slope and reservoir in the upstream level, the river live reaches.storage capacityFurthermore, decreases it is clearly gradually observed and the that dead by increasing storage remains the minimum intact. Inreservoir Scenarios level, S3 the and live S4, storagethe movement capacity of decreases the delta gradually reduced signiﬁcantly and the dead as storage the high remains water levelintact. slowed In Scenarios down theS3 and velocity S4, the in movementareas far upstream of the delta from reduced the dam, significantly thus promoting as the high deposition water level at those slowed locations. down the Table velocity5 shows in areas the farmean upstream advancement from the rate dam, of the thus Mangla promoting deltatoward deposition the damat those face. locations. Table 5 shows the mean advancement rate of the Mangla delta toward the dam face. Table 5. Delta advance rate towards the dam face. Table 5. Delta advance rate towards the dam face. Scenarios S1-4.5 S2-4.5 S3-4.5 S4-4.5 S5-4.5 Delta advancement rate (kmScenarios/year) 0.183S1-4.5 0.169 S2-4.5 0.150S3-4.5 S4-4 0.113.5 S5-4.5 0.131 Delta advancement rate (km/year) 0.183 0.169 0.150 0.113 0.131

Table6 shows the total volume lost as percentage of gross storage capacity (including the upraised Table 6 shows the total volume lost as percentage of gross storage capacity (including the Mangla Dam capacity) for the analyzed scenarios using future discharges derived from RCP4.5 and 8.5, upraised Mangla Dam capacity) for the analyzed scenarios using future discharges derived from which varies from 30.98% to 32.75%. The results reveal that there is a maximum difference of 1.77% in RCP4.5 and 8.5, which varies from 30.98% to 32.75%. The results reveal that there is a maximum the total storage lost among different operational scenarios. It also shows the impact between the two difference of 1.77% in the total storage lost among different operational scenarios. It also shows the sets of future discharges (RCP4.5 and 8.5) on reservoir sedimentation up to the year 2065 and has minor impact between the two sets of future discharges (RCP4.5 and 8.5) on reservoir sedimentation up to influence in view of comparative volume lost (i.e., <1%). Therefore, further results for the RCP4.5 are discussed because RCP8.5 has similar impact as RCP4.5 on Mangla Reservoir sedimentation.

Table 6. Total volume loss of the reservoir in percentage.

Total Volume Lost in % Scenarios Year 2025 Year 2035 Year 2045 Year 2055 Year 2065 S1-4.5 Scenario 1 with RCP4.5 19.43 22.95 25.81 28.34 30.98 S1-8.5 Scenario 1 with RCP8.5 19.13 22.46 25.48 28.01 30.32 S2-4.5 Scenario 2 with RCP4.5 19.44 22.98 25.85 28.45 31.01 S2-8.5 Scenario 2 with RCP8.5 19.14 22.52 25.56 28.13 30.74 S3-4.5 Scenario 3 with RCP4.5 19.51 23.14 26.11 29.21 31.79 S3-8.5 Scenario 3 with RCP8.5 19.20 22.73 25.80 28.81 31.06 S4-4.5 Scenario 4 with RCP4.5 19.57 23.29 26.36 29.57 32.75 S4-8.5 Scenario 4 with RCP8.5 19.27 22.78 25.84 28.98 32.38 S5-4.5 Scenario 5 with RCP4.5 19.45 23.17 26.25 29.46 32.45 S5-8.5 Scenario 5 with RCP8.5 19.14 22.55 25.66 28.81 31.03 Water 2020, 12, x FOR PEER REVIEW 20 of 28 the year 2065 and has minor influence in view of comparative volume lost (i.e., <1%). Therefore, further results for the RCP4.5 are discussed because RCP8.5 has similar impact as RCP4.5 on Mangla Reservoir sedimentation.

Table 6. Total volume loss of the reservoir in percentage.

Total Volume Lost in % Scenarios Year 2025 Year 2035 Year 2045 Year 2055 Year 2065 S1-4.5 Scenario 1 with RCP4.5 19.43 22.95 25.81 28.34 30.98 S1-8.5 Scenario 1 with RCP8.5 19.13 22.46 25.48 28.01 30.32 S2-4.5 Scenario 2 with RCP4.5 19.44 22.98 25.85 28.45 31.01 S2-8.5 Scenario 2 with RCP8.5 19.14 22.52 25.56 28.13 30.74 S3-4.5 Scenario 3 with RCP4.5 19.51 23.14 26.11 29.21 31.79 S3-8.5 Scenario 3 with RCP8.5 19.20 22.73 25.80 28.81 31.06 S4-4.5 Scenario 4 with RCP4.5 19.57 23.29 26.36 29.57 32.75 S4-8.5 Scenario 4 with RCP8.5 19.27 22.78 25.84 28.98 32.38 S5-4.5 Scenario 5 with RCP4.5 19.45 23.17 26.25 29.46 32.45 WaterS5-8.52020, 12 ,Scenario 2736 5 with RCP8.5 19.14 22.55 25.66 28.81 31.03 20 of 27

The results indicate that the total volume loss because of sedimentation is around 3.0 BCM in GSC Thefor all results the scenarios. indicate that In theview total of volumethese results loss because for all ofoperational sedimentation scenarios, is around sediment-trapping 3.0 BCM in GSC capacityfor all the is scenarios.high, but Inthere view is ofa small these difference results for am allong operational all of them scenarios, because sediment-trapping of the vast reservoir capacity area andis high, capacity. but there However, is a small the di storagefference loss among by sediment all of themation because in the main of the Mangla vast reservoir pocket area close and to the capacity. dam hasHowever, a greater the impact, storage as loss compared by sedimentation to the total in thevolume main lost Mangla due to pocket sedimentation close to the in dam the hasreservoir, a greater as illustratedimpact, as comparedin Figure 16. to theScenarios total volume S1 and lost S2 duehaving to sedimentation a minimum reservoir in the reservoir, level show as illustrated maximum in depositionFigure 16. Scenariosin the main S1 Mangla and S2 pocket having as a compared minimum to reservoir other scenarios level show until maximum 2045. Afterward, deposition a gradual in the fallmain has Mangla been pocketobserved as comparedbecause of to increase other scenarios sediment until outflows, 2045. Afterward,as shown ain gradual Figure fall17. hasFurther, been Scenariosobserved S3, because S4, and of S5 increase have progressively sediment outflows, increased as sediment shown in deposition Figure 17. in Further, the main Scenarios pocket (Figure S3, S4, 16)and with S5 have the lowest progressively sediment increased outflows sediment (Figure 17) deposition and minimum in the maindelta pocketadvancement (Figure rate 16) with the highestlowest sedimentreduced level outflows of sediment (Figure 17bed) and at the minimum pivot point delta (Figure advancement 15) as compared rate with theto Scenarios highest reduced S1 and S2.level This of sediment reveals bedthat atthe the rising pivot pointdrawdown (Figure level 15) as of compared the reservoir to Scenarios reduced S1 the and sediment S2. This reveals delta advancementthat the rising rate drawdown and kept level the ofpivot the reservoirpoint far away reduced from the the sediment dam face delta for advancement a longer period, rate but and it kept has alsothe pivotincreased point farthe away upstream from thebed dam level face because for a longer of high period, sedimentation. but it has also However, increased thekeeping upstream the minimumbed level becausedrawdown of high level sedimentation. in the reservoir However,triggers high keeping advancement the minimum of sediment drawdown deposits level toward in the thereservoir dam face. triggers high advancement of sediment deposits toward the dam face.

Water 2020, 12, x FOR FigureFigurePEER REVIEW 16. StorageStorage loss loss due due to to sedimentation sedimentation in in the the main main Mangla Mangla pocket. pocket. 21 of 28

FigureFigure 17. 17. TotalTotal sediment sediment inflows inﬂows and outflows outﬂows from the reservoir.

OutflowingOutflowing sand particles significantly significantly contribute to the wear and tear of the turbine blades and at the outlet surfaces.surfaces. TableTable7 7provides provides information information on on the the proportion proportion of sandof sand outflows outflows and and inflows inflows for forfour four scenarios. scenarios.

Table 7. Comparison of the sand outflow with respect to sand inflow in percentage.

Years S1-4.5 S2-4.5 S3-4.5 S4-4.5 S5-4.5 2025–2035 0 0 0 0 0 2035–2045 0.01 0 0 0 0 2045–2055 11.48 6.17 0 0 0 2055–2065 13.67 16.78 10.35 0 0.93

The sand outflows increase with time as lower drawdown levels are maintained and decreases with higher drawdown levels. Simulation S1 has the least drawdown level among the four simulations, demonstrating this problem to the fullest. Initially, the sand outflows are small during the decade 2035–2045 i.e., 0.005 million tons/year but after about a decade as the availability of sand increases, the outflows increase by about 14% to reach 9 million tons/year (Table 7). Further, no sand outflows were observed through the turbine intakes in Simulation S4 having a minimum reservoir level of 329 m a.s.l.; in Simulation S5, every year, the minimum drawdown level of the reservoir is increased by 0.75 m. Thus, the objective of deterring the volume loss by operating the reservoir at MOL may contravene the objective of passing less concentrated sediment water through turbine intake to prevent wear and tear and vice versa. The low drawdown level also increases the sand particles proportion in water. When this sand- laden water passes through the hydropower tunnels, it causes abrasion of the mechanical surfaces exposed to high-speed flow, e.g., to turbine blades. Generally, this phenomenon occurs for the particle size exceeding 0.1 mm but may also happen for smaller particles in case of high operation heads and angular quartz particles [6]. As a result, it may cause clogging or blocking of the outlets. The risk is higher if the dam is situated in a seismically active zone, as Mangla is, due to liquefaction of the accumulated sediments. The composition of the sediment outflow through the reservoir vary from the low flow season to the high flow season. During the low flow season (February and March), the outflow consists of coarse silt and sand, while in the high flow season (May and June), the composition changes to clay and fine silts. Maximum sediment outflows of coarse silt and sand from the reservoir therefore do not occur at the same time as the peak flows in the river. Hence, this coarser material is deposited in the river downstream of the dam and subsequently re-scoured once the higher flows are released from the dam later in the year. This depositing and reworking of the sediment in the river could cause additional and significant environmental damage. Furthermore, these highly concentrated sediment outflows are also very harmful to biota in downstream river reaches. Recent case studies have

Water 2020, 12, 2736 21 of 27

Table 7. Comparison of the sand outﬂow with respect to sand inﬂow in percentage.

Years S1-4.5 S2-4.5 S3-4.5 S4-4.5 S5-4.5 2025–2035 0 0 0 0 0 2035–2045 0.01 0 0 0 0 2045–2055 11.48 6.17 0 0 0 2055–2065 13.67 16.78 10.35 0 0.93

The sand outflows increase with time as lower drawdown levels are maintained and decreases with higher drawdown levels. Simulation S1 has the least drawdown level among the four simulations, demonstrating this problem to the fullest. Initially, the sand outflows are small during the decade 2035–2045 i.e., 0.005 million tons/year but after about a decade as the availability of sand increases, the outflows increase by about 14% to reach 9 million tons/year (Table7). Further, no sand outflows were observed through the turbine intakes in Simulation S4 having a minimum reservoir level of 329 m a.s.l.; in Simulation S5, every year, the minimum drawdown level of the reservoir is increased by 0.75 m. Thus, the objective of deterring the volume loss by operating the reservoir at MOL may contravene the objective of passing less concentrated sediment water through turbine intake to prevent wear and tear and vice versa. The low drawdown level also increases the sand particles proportion in water. When this sand-laden water passes through the hydropower tunnels, it causes abrasion of the mechanical surfaces exposed to high-speed flow, e.g., to turbine blades. Generally, this phenomenon occurs for the particle size exceeding 0.1 mm but may also happen for smaller particles in case of high operation heads and angular quartz particles [6]. As a result, it may cause clogging or blocking of the outlets. The risk is higher if the dam is situated in a seismically active zone, as Mangla is, due to liquefaction of the accumulated sediments. The composition of the sediment outflow through the reservoir vary from the low flow season to the high flow season. During the low flow season (February and March), the outflow consists of coarse silt and sand, while in the high flow season (May and June), the composition changes to clay and fine silts. Maximum sediment outflows of coarse silt and sand from the reservoir therefore do not occur at the same time as the peak flows in the river. Hence, this coarser material is deposited in the river downstream of the dam and subsequently re-scoured once the higher flows are released from the dam later in the year. This depositing and reworking of the sediment in the river could cause additional and significant environmental damage. Furthermore, these highly concentrated sediment outflows are also very harmful to biota in downstream river reaches. Recent case studies have estimated detailed biological and physical alterations, reductions of benthic organisms due to bed siltation, and severe fish mortality [68]. Table8 gives information regarding outflows of sand fraction, which is pertinent to the above-mentioned risks.

Table 8. Outﬂows of diﬀerent sand fractions from the reservoir for S1-4.5.

Very Fine Sand Fine Sand Medium Sand Coarse Sand Very Coarse Duration (0.0625–0.125 mm) (0.125–0.25 mm) (0.25–0.5 mm) (0.5–1 mm) Sand (1–2 mm) (Years) (M (M Tons/year) (M Tons/year) (M Tons/year) (M Tons/year) Tons/year) 2045–2055 0.671 0.037 0.005 0.001 0.001 2055–2065 0.849 0.042 0.007 0.001 0.002

The observed sediment deposition rate at the Mangla Reservoir from 2005–2017 was about 0.023 BCM per year, while rates of deposition for future years under impact of climate change with diﬀerent reservoir operational rules are show in Table9. The sediment deposition rate increases from 0.023 BCM to 0.032 BCM per year due to the increase in summer precipitation and discharges due Water 2020, 12, 2736 22 of 27 to climate change, which causes more weathering and erosion in the Mangla catchment area. It also indicates that the deposition rate decreases gradually with the passage of time because total sediments outﬂows are increasing, as shown in Figure 17.

Table 9. Prediction of sediment deposition (BCM/year).

Scenarios Year 2025–2035 Year 2035–2045 Year 2045–2055 Year 2055–2065 S1-4.5 0.032 0.026 0.023 0.024 S2-4.5 0.032 0.026 0.024 0.023 S3-4.5 0.033 0.027 0.028 0.023 S4-4.5 0.034 0.028 0.029 0.029 S5-4.5 0.034 0.028 0.029 0.027

To summarize, the minimum reservoir operation level has a significant impact on reservoir sedimentation. The results showed that, by keeping the reservoir MOL lower, a higher volume of water can be made availability for useful purposes e.g., irrigation and hydropower. However, it leads to a rapid increase of the bed levels near to the dam (Scenarios 1 and 2). Additionally, low MOL enhances the sediment outflows, which increases the risk of turbine abrasion and tunnel choking. An opposite measure is to keep the MOL high, which has the disadvantage of reducing water availability, but on a positive note, it slows down the delta advance (Scenarios 3 and 4). A further negative effect of high MOL is the increased foreset slope of the sediment delta, which might collapse, thus leading to mass movements of the bed load into the power tunnel(s). Considering all these factors, a strategy (Scenario S5) that leads to a gradual increase in MOL of the reservoir with time is the optimal strategy because it slowly introduces the change on yearly basis, arresting the delta advancement, preserving the live storage, and keeping the sand outflows within their reasonable limits.

5.3. General Lessons Learnt from the Present Study The present study results can be generalized to multi-purpose reservoirs situated in water stressed, arid, or semi-arid zones. The reservoir can have multiple objectives e.g., irrigation supplies, hydropower, flood control, etc. It is also well known that streams in arid zones carry far more sediment than temperate regions that arrive in great quantities during flood seasons. Thus, reservoir sediment management has a very vital role to play in prolonging the life of a reservoir. From the reservoir operations viewpoint, it is of paramount importance to ensure sustainable use of the reservoir by reducing sedimentation and not allowing sediment to approach the power tunnels or water intakes too closely. This would prevent the clogging of the latter. The threat is all the more palpable in a seismically active zone where sand liquefaction can cause highly turbid water to invade the installations, leading to its shut-off and degradation. The sediment management is greatly dependent upon the reservoir operations policy, which fixes the reservoir minimum water level. If the water level is low, more turbid water can be made to pass through the tunnels, thus promoting flushing of the sediment. However, it has the inconvenience of allowing the sediment to deposit more closely to the tunnels/intakes and degrading the turbines. The other option is to keep the reservoir water level high, which may not be possible due to end-users being dependent on the water, or, the reservoir has to be depleted to a certain level before the arrival of a flood season to store a flood peak. Further, the high water level can cause sediment mounds to form far from the intakes, but this also increases the susceptibility to slope failure or slumping. The MOL may be set low in initial years after the commissioning of the reservoir, but it may have to be raised gradually to reduce the sediment accumulation in later years. The suggestions that can be advanced based on the present case study recognize the fact that flows may need to be passed through the installations housing hydraulic machinery; however, at the design stage, it would be prudent to reserve some low-level outlets only for flushing and not to install turbines on those. A maximum “shut off” sediment load has to be defined for an installation at which it must Water 2020, 12, 2736 23 of 27 be immediately shut down to protect the installation. To avoid the risk of installation clogging during an earthquake or slumping, a “deep water suction dredging system” of adequate capacity should be procured in advance to clear the blockage as soon as possible. Further, regular repair of exposed hydraulic surfaces should be undertaken to repair the damage done by silt-laden water. The turbines selected should be able to withstand the high sediment load. Further, options may be explored to protect the machinery through specialized coatings e.g., ceramics and polyurethane. Furthermore, outflows near to the MOL should be strictly monitored to assess its effect on the downstream fish and benthic organisms, and measures may be taken to protect the vulnerable species during the occurrence of such flows.

6. Conclusions This study evaluates the impact of climate change and reservoir operation curves on reservoir sedimentation and its sustainable management. Reservoir sedimentation management is a complicated and challenging task because some objectives might controvert others. For instance, the objective of deterring the volume loss may contravene the objective of passing less concentrated sediment water through turbine intake to prevent wear and tear and vice versa. To assess the impact of different operation strategies with future discharges derived through RCP4.5 and 8.5 under the impact of climate change, long-term numerical modeling has been carried out by the HEC-RAS model to assess the sediment dynamics in the reservoir. As a case study, the Mangla Reservoir in northern Pakistan, having complex geometry and critical sediment-related issues, was selected. This study used an ANN technique over conventional SRCs method to estimate the missing past data and future suspended sediment load. Results showed that the ANN-based model outperformed the SRCs method, scoring relatively higher values of NSE-0.78 (ANN) to 0.29 (SRC). This shows that SRCs underestimate the sediment load, and ANN gave best estimation of the suspended sediment load. Results indicate that the 1D HEC-RAS numerical model has described well the behavior of the reservoir sedimentation. Sensitivity analysis showed that the most appropriate values of Manning’s roughness coefficient and computational time step are identified as 0.03 and 6 h, respectively. It is observed that a nonlinear relation exists between reservoir sedimentation and the Manning’s roughness coefficient. Further, the most suitable sediment transport function and fall velocity method for the Mangla Reservoir are identified as Ackers and White and Van Rijn, respectively. The second-best formula after Ackers and White was that of Laursen. The simulated outcomes match with the estimations based on observations within the expected tolerances. Some limitations are observed when estimating bed levels on the river bends and near the dam because of 3D flow phenomena e.g., turbidity currents, as these could not be simulated using 1D models. Results showed that reservoir storage capacity has been reduced every year, and the delta of the deposited sediment has advanced toward the dam face at different rates (0.113–0.183 km/year) under projected flows of RCP4.5 and 8.5 and with various reservoir operational scenarios. Reservoir lost its maximum total storage capacity (9.11 BCM) by 32.75% and 31.80% by keeping the reservoir MOL at 329 m a.s.l. under RCP4.5 and 8.5, respectively. Minimum storage loss (30%) has been found in Scenario1 (MOL 320 m a.s.l.), but it fills the dead storage of the reservoir earliest than other cases. Simulation results showed that Scenario 5, in which every year MOL is increased by 0.75 m, significantly slows down the delta advance and also deters the transport of sand particles into turbine intakes. Thus, by increasing the MOL of the reservoir by a certain value every year, the delta movement rate and the bed level at the upstream of the dam can be checked, which lessens the risk of tunnel choking. Hence, the probability of mass movements of coarse silts and fine sands into the power tunnels can be controlled by raising the MOL by certain level ( 0.75 m) every year. Consequently, reservoir ≥ operation rules and their outcomes regarding reservoir sedimentation pattern have to be considered most important parameter in view of sustainable reservoir management. Reservoir operation rules provides optimal solutions to the environmental, economic, and social requirements related to reservoir. Water 2020, 12, 2736 24 of 27

Recommendations: Detailed hydrographic surveys of the main pocket close to dam will be performed to investigate the exact position of sediment delta pivot point with its reduced level and bed levels near to turbine intakes will be measured at least once per year. To avoid the slumping or mass movements towards the dam face, reservoir MOL will be raised by certain amount every year for reduction of delta rate. As the Mangla Reservoir has greater amount of silt deposits in main pocket, it is recommended to pass these sediments every year as a maximum wash load through intake tunnels during MOL. Further, in order to prevent the entry of slide mass into power tunnels, construction of sub-aqueous dyke will be considered as an effective protective measure for the near future. At last replacing of older turbines with modern turbines that are designed for highly erosion resistant material will be installed. Author Contributions: Conceptualization, M.A.K. and J.S.; methodology, M.A.K., J.S. and S.H.; software, M.A.K.; validation, M.A.K.; formal Analysis, M.A.K.; investigation, M.A.K.; data curation, M.A.K. and S.H.; writing-original draft preparation, M.A.K.; writing-review & editing, J.S. and S.H.; visualization, S.H.; supervision, J.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The first author was financially supported by the doctoral scholarship from the Higher Education Commission of Pakistan (HEC) and the German Academic Exchange Service (DAAD). The authors wish to acknowledge the Mangla Dam Authority, the Surface Water Hydrology Project (SWHP) of WAPDA, and Naeem Saddique for providing data that was used in this study. The first author is also thankful to Torsten Heyer from IWD, TU Dresden for their constructive guidance, suggestions and cooperation during research work. We extend our thanks to the Institute of Hydraulic Engineering and Technical Hydromechanics (IWD), Technische Universität Dresden, for providing the opportunity to perform this research. Open Access Funding by the Publication Fund of the TU Dresden. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Babur, M.; Shrestha, S.; Bhatta, B.; Datta, A.; Ullah, H. Integrated assessment of extreme climate and landuse change impact on sediment yield in a mountainous transboundary watershed of India and Pakistan. J. Mt. Sci. 2020, 17, 624–640. [CrossRef] 2. Azmat, M.; Wahab, A.; Huggel, C.; Qamar, M.U.; Hussain, E.; Ahmad, S.; Waheed, A. Climatic and hydrological projections to changing climate under CORDEX-South Asia experiments over the Karakoram-Hindukush-Himalayan water towers. Sci. Total Environ. 2020, 703, 135010. [CrossRef] [PubMed] 3. Anari, R.; Hotchkiss, R.H.; Langendoen, E.J. Elements for the successful computer simulation of sediment management strategies for reservoirs. Water 2020, 12, 714. [CrossRef] 4. White, R. Evacuation of Sediments from Reservoirs; Thomas Telford Publishing: London, UK, 2001. [CrossRef] 5. Wang, G.; Wu, B.; Wang, Z.Y. Sedimentation problems and management strategies of sanmenxia reservoir, yellow river, china. Water Resour. Res. 2005, 41.[CrossRef] 6. Morris, G.L.; Fan, J. Reservoir Sedimentation Handbook: Design and Management of Dams, Reservoirs, and Watersheds for Sustainable Use; McGraw Hill Professional: New York, NY, USA, 1998. 7. Sabir, M.A.; Shaﬁq-Ur-Rehman, S.; Umar, M.; Waseem, A.; Farooq, M.; Khan, A.R.J. The impact of suspended sediment load on reservoir siltation and energy production: A case study of the Indus River and Its tributaries. Pol. J. Environ. Stud. 2013, 22, 219–225. 8. Abdallah, M.; Stamm, J. Evaluating the impacts of sedimentation and water balance computations on reservoir operation, a case study of Girba Dam-Atbara River. Nile Basin Water Sci. Eng. J. 2012, 5, 14–24. 9. Lai, J.-S.; Shen, H.W. Flushing sediment through reservoirs. J. Hydraul. Res. 1996, 34, 237–255. [CrossRef] 10. Kawashima, S.; Johndrow, T.; Annandale, G.; Shah, F. Reservoir conservation volume I: The RESCON approach, economic and engineering evaluation of alternative strategies for managing sedimentation in storage reservoirs. In A Contribution to Promote Conservation of Water Storage Assets Worldwide. The International Bank for Reconstruction and Development; The World Bank: Washington, DC, USA, 2003. 11. Schleiss, A.J.; Franca, M.J.; Juez, C.; De Cesare, G. Reservoir sedimentation. J. Hydraul. Res. 2016, 54, 595–614. [CrossRef] 12. Garcia, M. Sedimentation engineering: Processes, measurements, modeling, and practice. Am. Soc. Civ. Eng. 2008.[CrossRef] 13. Annandale, G.W.; Morris, G.L.; Karki, P. Extending the Life of Reservoirs: Sustainable Sediment Management for Dams and Run-of-River Hydropower; The World Bank: Washington, DC, USA, 2016. [CrossRef] Water 2020, 12, 2736 25 of 27

14. Petkovsek, G.; Roca, M. Impact of reservoir operation on sediment deposition. In Proceedings of the Institution of Civil Engineers-Water Management; ICE Publishing: London, UK, 2014; pp. 577–584. [CrossRef] 15. Rehman, M.A.; Naeem, U.A.; Hashmi, H.N.; Shakir, A.S. Possible options to slow down the advancement rate of Tarbela delta. Environ. Monit. Assess. 2018, 190, 39. [CrossRef] 16. Iqbal, M.; Ghumman, A.R.; Hashmi, H.N.; Haider, S. Planning of Hydraulic Flushing Schedule for Prolonging the Life of a Hydropower Plant. IJST-T Civ. Eng. 2019, 43, 487–501. [CrossRef] 17. Simoes, F.; Yang, C. Sedimentation modeling for rivers and reservoirs. In Erosion and Sedimentation Manual; United States Department of the Interior: Denver, CO, USA, 2006. 18. Ferreira da Silva, A.M.; Ebrahimi, M. Meandering morphodynamics: Insights from laboratory and numerical experiments and beyond. Am. Soc. Civ. Eng. 2017.[CrossRef] 19. USACE. HEC-RAS River Analysis System Hydraulic Reference Manual. Version 5.0. In Hydrologic Engineering Center Davis; US Army Corps of Engineers, Hydrologic Engineering Center: Davis, CA, USA, 2016. 20. Mahmood, R.; Jia, S. Assessment of impacts of climate change on the water resources of the transboundary Jhelum River basin of Pakistan and India. Water 2016, 8, 246. [CrossRef] 21. Babur, M.; Babel, M.S.; Shrestha, S.; Kawasaki, A.; Tripathi, N.K. Assessment of climate change impact on reservoir inflows using multi climate-models under RCPs—The case of Mangla Dam in Pakistan. Water 2016, 8, 389. [CrossRef] 22. Saddique, N.; Usman, M.; Bernhofer, C. Simulating the Impact of Climate Change on the Hydrological Regimes of a Sparsely Gauged Mountainous Basin, Northern Pakistan. Water 2019, 11, 2141. [CrossRef] 23. Butt, M.J.; Mahmood, R.; Waqas, A. Sediments deposition due to soil erosion in the watershed region of Mangla Dam. Environ. Monit. Assess. 2011, 181, 419–429. [CrossRef] 24. Sarwar, S. Reservoir Life Expectancy in Relation to Climate and Land-Use Changes: Case Study of the Mangla Reservoir in Pakistan. Ph.D. Thesis, University of Waikato, Hamilton, New Zealand, 2013. 25. Iqbal, M.; Ghumman, A.R.; Haider, S.; Hashmi, H.N.; Khan, M.A. Application of Godunov type 2D model for simulating sediment flushing in a reservoir. Arab. J. Sci. Eng. 2019, 44, 4289–4307. [CrossRef] 26. Mastrandrea, M.D.; Field, C.B.; Stocker, T.F.; Edenhofer, O.; Ebi, K.L.; Frame, D.J.; Held, H.; Kriegler, E.; Mach, K.J.; Matschoss, P.R. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. 2010. Available online: https://pure.mpg.de/rest/items/item_2147184/ component/file_2147185/content (accessed on 3 February 2020). 27. Azmat, M.; Choi, M.; Kim, T.-W.; Liaqat, U.W. Hydrological modeling to simulate streamflow under changing climate in a scarcely gauged cryosphere catchment. Environ. Earth Sci. 2016, 75, 186. [CrossRef] 28. Archer, D.R.; Fowler, H.J. Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan. J. Hydrol. 2008, 361, 10–23. [CrossRef] 29. Siddiqui, M.J.; Haider, S.; Gabriel, H.; Shahzad, A. Rainfall–runoff, flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin. Hydrol. Sci. J. 2018, 63, 1976–1997. [CrossRef] 30. Di Silvio, G.; Zanke, U.C.; Wang, Z.Y. World association for sedimentation and erosion research (WASER). Int. J. Sediment Res. 2016, 2. Available online: http://www.cnki.com.cn/Article/CJFDTotal-GJNS201602002.htm (accessed on 7 September 2019). 31. Sheikh, M.S. Mangla Dam—Past, Present & Future. In Proceedings of the Centenary Celebration (1912–2012), Pakistan, 2012; pp. 131–147. Available online: https://pecongress.org.pk/images/upload/ books/6-M.%20Saleem%20Sheikh.pdf (accessed on 13 October 2019). 32. Saddique, N.; Bernhofer, C.; Kronenberg, R.; Usman, M. Downscaling of CMIP5 models output by using statistical models in a data scarce mountain environment (Mangla Dam Watershed), Northern Pakistan. Asia Pac. J Atmos Sci. 2019, 55, 719–735. [CrossRef] 33. Binnie, G.; Gerrard, R.; Eldridge, J.; Kirmani, S.; Davis, C.; Dickinson, J.; Gwyther, J.; Thomas, A.; Little, A.; Clark, J. Mangla. Part 1. Engineering Of Mangla. Proc. Inst. Civ. Eng. 1967, 38, 338–544. [CrossRef] 34. Binnie, G.; Gerrard, R.; Eldridge, T.; Kirmani, S.; Davis, C.; Paton, T.; Penman, A.; Skempton, A.; Copping, T.; Bartlett, C. Mangla: Part 1 and Part 2. Discussion: Includes corrigenda. Proc. Inst. Civ. Eng. 1968, 41, 119–202. [CrossRef] 35. Porterfield, G. Computation of fluvial-sediment discharge. Techniques of Water-Resources Investigations of the United States Geological Survey, Book 3. US Geol. Surv. 1972.[CrossRef] 36. Rantz, S.E. Measurement and Computation of Streamflow; US Department of the Interior, Geological Survey: Washington, DC, USA, 1982; Volume 2175. Water 2020, 12, 2736 26 of 27

37. Edwards, T.K.; Glysson, G.D.; Guy, H.P.; Norman, V.W. Field Methods for Measurement of Fluvial Sediment; US Geological Survey: Denver, CO, USA, 1999. 38. Colby, B. Relationship of Sediment Discharge to Streamflow; 2331–1258; US Dept. of the Interior, Geological Survey, Water Resources Division: Washington, DC, USA, 1956. [CrossRef] 39. Roushangar, K.; Shahnazi, S. Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches. J. Mt. Sci. 2020, 17, 480–491. [CrossRef] 40. Ateeq-Ur-Rehman, S.; Bui, M.D.; Hasson, S.U.; Rutschmann, P. An innovative approach to minimizing uncertainty in sediment load boundary conditions for modelling sedimentation in reservoirs. Water 2018, 10, 1411. [CrossRef] 41. Tarar, Z.R.; Ahmad, S.R.; Ahmad, I.; Khan, Z.M.; Washakh, R.M.A.; Ateeq-Ur-Rehman, S.; Bui, M.D. Effect of Sediment Load Boundary Conditions in Predicting Sediment Delta of Tarbela Reservoir in Pakistan. Water 2019, 11, 1716. [CrossRef] 42. Abdallah, M.; Stamm, J. Developing an optimum multipurpose reservoir operation policy under uncertain conditions. Nile Basin Water Sci. Eng. J. 2012, 5, 35–47. 43. Roca, M. Tarbela Dam in Pakistan. Case Study of Reservoir Sedimentation. Available online: http: //eprints.hrwallingford.com/id/eprint/891 (accessed on 21 May 2019). 44. Bennett, N.D.; Croke, B.F.; Guariso, G.; Guillaume, J.H.; Hamilton, S.H.; Jakeman, A.J.; Marsili-Libelli, S.; Newham, L.T.; Norton, J.P.; Perrin, C.J.E.M.; et al. Characterising performance of environmental models. Environ. Model. Softw. 2013, 40, 1–20. [CrossRef] 45. Rahman, K.U.; Shang, S.; Shahid, M.; Wen, Y. Hydrological evaluation of merged satellite precipitation datasets for streamflow simulation using SWAT: A case study of Potohar Plateau, Pakistan. J. Hydrol. 2020, 125040. [CrossRef] 46. Glysson, G.D. Sediment-Transport Curves. U.S. Geological Survey. 1987. Available online: https://pubs.usgs. gov/of/1987/0218/report.pdf (accessed on 9 December 2019). 47. Hussain, D.; Hussain, T.; Khan, A.A.; Naqvi, S.A.A.; Jamil, A. A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin. Earth Sci. Inform. 2020, 1–13. [CrossRef] 48. Yaseen, Z.M.; Naganna, S.R.; Sa’adi, Z.; Samui, P.; Ghorbani, M.A.; Salih, S.Q.; Shahid, S. Hourly river flow forecasting: Application of emotional neural network versus multiple machine learning paradigms. Water Resour Manag. 2020, 1–17. [CrossRef] 49. Pham, B.T.; Le, L.M.; Le, T.-T.; Bui, K.-T.T.; Le, V.M.; Ly, H.-B.; Prakash, I. Development of advanced artificial intelligence models for daily rainfall prediction. Atmos. Res. 2020, 237, 104845. [CrossRef] 50. Moradi, A.M.; Dariane, A.B.; Yang, G.; Block, P. Long-range reservoir inflow forecasts using large-scale climate predictors. Int. J. Climatol. 2020.[CrossRef] 51. Üne¸s,F.; Demirci, M.; Ta¸sar, B.; Kaya, Y.Z.; Varçin, H. Estimating dam reservoir level fluctuations using data-driven techniques. Pol. J. Environ. Stud. 2019.[CrossRef] 52. Ladanu, A.A.; Akanmu, S.; Adeyemo, J. Enhancing artificial neural network with multi-objective evolutionary algorithm for optimizing real time reservoir operations: A review. Am. J. Water Resour. 2020, 8, 118–127. [CrossRef] 53. Malik, A.; Kumar, A.; Kisi, O.; Shiri, J. Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling. Environ. Sci. Pollut. Res. 2019, 26, 22670–22687. [CrossRef] 54. Roshni, T.; Jha, M.K.; Drisya, J. Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural. Comput. Appl. 2020, 1–18. [CrossRef] 55. Chow, V.T. Open Channel Hydraulics; McGraw-Hill: New York, NY, USA, 1959; pp. 26–27. [CrossRef] 56. Ackers, P.; White, W.R. Sediment transport: New approach and analysis. J. Hydraul. Div. 1973, 99, 2041–2060. 57. Laursen, E.M. The total sediment load of streams. J. Hydraul. Div. 1958, 84, 1–36. 58. Engelund, F.; Hansen, E. A Monograph on Sediment Transport in Alluvial Streams. Technical University of Denmark Ostervoldgade 10, Copenhagen K. 1967. Available online: http://resolver.tudelft.nl/uuid: 81101b08-04b5-4082-9121-861949c336c9 (accessed on 5 January 2020). 59. Meyer-Peter, E.; Müller, R. Formulas for Bed-Load Transport. In Proceedings of the IAHSR 2nd Meeting, Stockholm, Appendix 2. 1948. Available online: http://resolver.tudelft.nl/uuid:4fda9b61-be28-4703-ab06- 43cdc2a21bd7 (accessed on 10 January 2020). Water 2020, 12, 2736 27 of 27

60. Copeland, R.R.; Thomas, W.A. Corte Madera Creek Sedimentation Study. US Army Engineer Waterways Experiment Station, Vicksburg, MS. TR-HL-89-6. 1989. Available online: http://hdl.handle.net/11681/13578 (accessed on 11 January 2020). 61. Yang, C.T. Incipient motion and sediment transport. J. Hydraul. Div. 1973, 99, 1679–1704. 62. Yang, C.T. Unit stream power equation for gravel. J. Hydraul. Eng. 1984, 110, 1783–1797. [CrossRef] 63. Toﬀaleti, F.B. A Procedure for Computation of the Total River Sand Discharge and Detailed Distribution, Bed to Surface; Committee On Channel Stabilization (ARMY): 1968. Available online: https://hdl.handle.net/ 11681/1822 (accessed on 12 January 2020). 64. Wilcock, P.R.; Crowe, J.C. Surface-based transport model for mixed-size sediment. J. Hydraul. Eng. 2003, 129, 120–128. [CrossRef] 65. Raza, R.A.; Khan, N.M.; Akhtar, N. Exploring sediment management options of Mangla Reservoir using RESSASS. Sci. Int. 2015, 27, 3347–3352. 66. Krone, R.B. Flume Studies of Transport of Sediment in Estrarial Shoaling Processes; Final Report; Hydraulic Engineering Laboratory and Sanitary Engineering Research Laboratory, University of California: Berkeley, CA, USA, 1962. 67. Partheniades, E. Erosion and deposition of cohesive soils. J. Hydraul. Div. 1965, 91, 105–139. 68. Espa, P.; Crosa, G.; Gentili, G.; Quadroni, S.; Petts, G. Downstream ecological impacts of controlled sediment ﬂushing in an Alpine valley river: A case study. River Res. Appl. 2015, 31, 931–942. [CrossRef]

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