Multimodelling approach to the assessment of climate change impacts on hydrology and river morphology in the Chindwin River Basin, Myanmar

Catena 188 (2020) 104464

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Multimodelling approach to the assessment of climate change impacts on hydrology and river morphology in the Chindwin River Basin, Myanmar

T

Sangam Shresthaa, , Naditha Imbulanaa, Thanapon Pimanb, Somchai Chonwattanac, Sarawut Ninsawata, Muhammad Babura ⁎

a

School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Stockholm Environment Institute, Asia Center, Chulalongkorn Soi 64, Phyathai Road, Pathumwan, Bangkok 1033, Thailand c Danish Hydraulic Institute (DHI) - Thailand, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand b

ARTICLE INFO

ABSTRACT

Keywords: Climate change Extreme events Hydrology Hydrodynamic Morphology Chindwin River

In the tropical region, climate change significantly affects the morphology of river channels as well as water resources. In this study, the climate change impact on hydrology and morphology is assessed in the Chindwin River Basin, Myanmar. Future climate data was developed by an ensemble of four Regional Climate Models (RCMs) using a performance-based weight method, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Two models were developed: a hydrological model (MIKE NAM) to simulate future discharge and a morphology model (MIKE 21 Flow Model) to simulate morphological changes under climate change scenarios. The results reveal that the mean annual discharge over the period from 2018 to 2040 (23 years) is projected to increase by 13.9 and 22.8%, under RCPs 4.5 and 8.5, respectively. The annual hydrograph shows a sharp rise and the recession of monsoon flood, with multiple peaks replacing the single peak observed during the baseline period (1975–2005) with a delay in peak of 8–10 days. The extreme events (floods) are expected to get more severe in the future in terms of frequency and magnitude compared to the baseline period. The study further shows that under extreme rainfall events, the left bank of the upstream areas is projected to experience severe morphological changes, accompanied by severe flood damage. As high flows become amplified due to climate change in the near future, morphological changes will exacerbate in the same period. Sediment deposition will increase the flood risk and river bank instability, constraining navigability in the river and adversely affecting riverine communities. The study reveals that mathematical modelling helps to identify the impacts of climate change on hydrology and morphology, and that knowledge coupled with an assessment of the vulnerability associated with lives and livelihoods, could help planners and policymakers to develop adaptation strategies and take meaningful action to mitigate the impact.

1. Introduction Climate change is currently affecting lives and livelihoods and will continue to do so in the foreseeable future. By the mid-twenty-first century, monsoon-related extremes are projected to rise in Southeast Asia under the RCP8.5 scenario (Hijioka et al., 2014). In Myanmar, observed and projected climate is characterised by increases in temperature and total rainfall, shorter duration and more frequent monsoon and severe extreme events (UNEP, 2012). Moreover, an increase in rainfall variability is projected during the rainy season, with a decrease from December to February (UNEP, 2012). Water resources and the hydrological cycle will be considerably modified in future (Xia et al., 2017; Shen et al., 2008). Small changes in

⁎

precipitation patterns induce larger changes in streamflow (Ashmore and Church, 2001), as well as modifying the streamflow regime and processes (Gharbi et al., 2016; Eaton and Lapointe, 2001; Ghimire and Higaki, 2015). As a result, the uncertainties in timing, magnitude, distribution, and geographical location of floods will increase (Bindoff et al., 2013). Shrestha and Htut (2016) state that the effects of climate change on streamflow are more significant than those caused by land use changes. A study carried out in the Yellow River shows that during the period from 1977 to 2005, there have been large reductions in river flow, and sediment discharges into the Bohai Sea. This is attributed to the decrease in precipitation, increase in air temperature and several human interventions (Jiang et al., 2017). Changes in streamflow and fluvial

Corresponding author. E-mail address: [email protected] (S. Shrestha).

https://doi.org/10.1016/j.catena.2020.104464 Received 26 January 2019; Received in revised form 2 January 2020; Accepted 8 January 2020 0341-8162/ © 2020 Elsevier B.V. All rights reserved.

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Fig. 1. Location of the Chindwin River Basin with hydro-meteorological stations (left) and the catchment delineation for the basin hydrology model (right).

processes have a huge effect on the modification of river morphology (Knox, 1993; Ashmore and Church, 2001; Karamouz et al., 2008; Hossain et al., 2015). Accordingly, climate change indirectly induces river morphological changes (Jiang et al., 2017; Karamouz et al., 2008; Ashmore and Church, 2001). The modification of streamflow causes changes in flow velocity, bed erosion and deposition as well as disturbing the sediment balance (Rahman et al., 2010). However, whether the flow is causing a net accretion or erosion is difficult to say without quantifying its impact (Uddin and Basak, 2012). Additionally, other than climate change associated phenomena, geology, topography, land use practices, and other human influences contribute to the flood regime and sedimentation patterns (Verhoog, 1987; Ashmore and Church, 2001; Erlandsson and Pedersen, 2016). Thus, it is evident that climate change can alter hydrology and the hydraulic regime, eventually resulting in river morphological changes. Understanding river morphology presents a challenge to the scientific community. It is defined by inter-related physical factors such as channel patterns and forms that are influenced by discharge, flow velocity, river bed composition and more (Fashae and Faniran, 2015). Furthermore, it could be influenced by humans mainly through land use practices. Mathematical models built using appropriate numerical techniques with logical reasoning can accurately and efficiently simulate different scenarios. According to Chang (2008), modelling is recognised as a modern technique for understanding both short-term and long-term changes in river course due to changes in the environment. Hydrodynamic patterns, sediment transportation, and morphological changes at different time scales can be accurately simulated with the use of suitable model parameters (Luan et al., 2017; Sanyal, 2017). Mathematical modelling can be used to identify the extent of river channel changes and assist policymakers and planners in the design of sustainable water resources development plans (Yossef et al., 2008;

Rahman et al., 2010; Luan et al., 2017). Changes in the streamflow/fluvial processes and river morphology can influence the river environment, infrastructure and society. They can cause structural damage, as well as the loss of riparian land, and livelihoods. Changes to river channel geometry can disturb transport facilities and other public utilities (Bashir, 2013), altering flood-prone area demarcations, and land values (Karamouz et al., 2008). Wetlands and marine life can be affected and, consequently, the associated livelihoods. River morphology changes have been identified (Kravtsova et al., 2009) as one of the key challenges in the Chindwin River Basin, which is the focus of this research. A remote sensing study carried out by the Myanmar Environment Institute (MEI) and Stockholm Environment Institute (SEI) Asia in this basin has identified the “dancing” (changing its course) parts of the river over the years, establishing that it is morphologically dynamic. Given the geomorphologic features of the rivers, channel migration was identified as prone to channel changes and migration in both Ayeyarwady and Chindwin closer to the confluence. This is also evident from images showing the channel path over the past years (Hydro-Informatics Centre [HIC], 2017). The Chindwin River’s morphological changes have caused disruption to the livelihood of the community in the past (Mann, 2013; Meel et al., 2014). The socio-economic damage to the community caused by river morphological changes includes decreased navigability, loss of community and agriculture lands, threat to fish production, and flood. The uncertainties in future scenarios of climate along with other changes in land use may induce changes in river morphology, aggravating the related socioeconomic issues in the Chindwin River Basin. Accurate prediction of morphological changes is important for the sustainability of the large riverine population. In order to predict the behaviour of river systems, the sensitivity of basin hydrology to climate change and the sensitivity of river morphology to hydrology need to be 2

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Fig. 2. Methodological framework for assessing the climate, hydrology, and morphological changes from extreme climate events under climate change scenarios in the near future.

understood (Verhoog, 1987; Ashmore and Church, 2001). However, a review of the literature reveals the inadequacy of research in this field, particularly regarding Chindwin, thus limiting meaningful interventions to find sustainable solutions to the problems resulting from morphological changes. Therefore, this study is conducted with the objective of understanding the hydrological, river hydraulics, and morphological changes in the near future under climate change scenarios to help basin planners and policymakers adopt sustainable mitigation and adaptation measures.

which is the widest part of the river (HIC, 2017). The majority of the basin is covered by forest; 50% with closed forest and another 33% covered by shrub forest. Agricultural land, alluvial island cultivation, and homestead gardening contributes to 15% of the basin area. Nearly, 2% of land area is under shifting cultivation and swamp areas (Latt and Wittenberg, 2015). Major crops include: paddy, wheat, maize, millet, groundnut, sesame, cotton, pulses, and other vegetables. The total population of the basin is 6 million of which 2.5 m live in the Indian territory, and 3.5 m reside in Myanmar (SEI, 2016). The river has 730 km of navigable length.

2. Study area

3. Data and method

The Chindwin River Basin is located in North-Western Myanmar between latitude 22°06′–26°00′ N and longitude 94°18′–95°42′ E (Latt et al., 2015). The Chindwin River is the largest tributary of the Ayeyarwady River and is the third largest river in Myanmar. It is often considered to be a separate river basin (Hennig, 2016). The catchment area of the Chindwin River Basin extends over 114,112 km2, of which 15% is in Indian territory. This catchment area spreads over 46 townships, 13 districts, and four regions namely: Sagaing, Kanchin, Chin, and Magway (Fig. 1). The annual surface water resources in the basin equate to 149.7 km3 (SEI, 2016). The main river is 900 km long. The river basin is constituted mainly of tertiary continental sediments (Latt and Wittenberg, 2015). The climate in Myanmar varies from tropical to subtropical. The country experiences three seasons; Inter-monsoon (hot and dry) from March to April, Southwest monsoon (wet) from May to October, and Northeast monsoon (cold and dry) from November to February (Union of Myanmar, 2009). Chindwin receives from 1500 to 4200 mm varying spatial precipitation on average. The southeastern part of the basin lies in the dry zone of Myanmar and receives irregular and scarce rainfall. The monthly average temperature of the basin varies from 23 °C in the upper region to 28 °C in the lower region close to Monywa (SEI, 2016). Average annual flow at Monywa (Fig. 1) is 4,500 m3/s. This is about 40% of the average annual flow of the Ayeyarwady River. Major floods in the basin generally occur from July to September (SEI, 2016). The Chindwin River, the largest tributary of the Ayeyarwady River, has tectonic controls (Wang et al., 2014), and a rock cut river channel with a mild gradient of less than 0.25%. Moving from the headwaters of the Chindwin River to the Chindwin-Ayeyarwady, the channel confluence type varies. Starting from a meandering type, it transforms to a single channel at around 80 km downstream, then shows some braiding mid-way (about 420 to 570 km downstream) with anabranching close to the confluence. This latter reach is the area of focus for the morphological change study in this research where the river confinement is described as semi-confined. The river starts from a width of about 150 m and broadens gradually as it descends. The river channel width from Monywa to the confluence (focus area) varies from 706 to 960 m

The study projects the climate and hydrology of the basin under climate change scenarios and assesses the morphological changes under extreme climate and streamflow events in the near future. The methodology primarily involves the bias correction of Regional Climate Model (RCM) data, producing a multimodel ensemble of climate, with a combination of different mathematical modelling techniques for hydrological and morphological assessment. The near future climate under RCP4.5 and RCP8.5 scenarios was analysed and compared against the baseline climate to understand the extent of climate change in the basin. Three climate indicators were used: rainfall, daily maximum temperature (Tmax), and daily minimum temperature (Tmin). Next, a basin-wide hydrological model was developed to analyse the climate change impacts on basin hydrology. In the final stage, a river morphological model was developed to analyse the morpho-dynamical changes caused by extreme climate events in the near future under climate change scenarios. An overview of the methodology can be summarised as shown in Fig. 2 and is further discussed in detail under this section. 3.1. Climate change projection Future climate for the basin was projected by obtaining data from selected RCMs and applying bias correction. The baseline period for climate change analysis was selected as 1975–2005. Data for the baseline period and near future (2011–2040) under RCP4.5 and RCP8.5 was obtained for four RCMs. This data was downloaded from the World Climate Research Program (WCRP) using the Coordinated Regional Downscaling Experiment (CORDEX) program website for the East Asia domain. The selected RCMs consisted of the semi-implicit, ConformalCubic Atmospheric Model (CCAM), the semi-Lagrangian atmospheric model developed by CSIRO, (McGregor et al., 2008; 2016). Details of the selected RCMs are summarised in Table 1. Five stations were used to extract the downloaded data: Hkamti, Homalin, Mawlaik, Kalewa, and Monywa (Fig. 1). Among various bias correction methods ranging from simple scaling approaches to more complex methods employing probability mapping, linear scaling 3

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month, Phist , m, d and Pfut , m, d are respectively the raw RCM precipitation of the dth day of the mth month during the historical run and the future forecast, µ (Pobs, m) and µ (Phst , m) are the long-term monthly mean of the mth month of the observed data and RCM simulated data of the historical run respectively. On many occasions, a multimodel RCM ensemble has provided better results than single RCM simulation (Feng et al., 2011; Suh et al., 2012). A performance-based weight assigning method was used for this study to obtain the ensemble mean. This method is entitled “Performance-based ensemble averaging using RMSE and absolute correlation coefficient”, hereafter referred to as PEA_RAC. The PEA_RAC method outperforms others such as multivariate regression and equal weight assignment (Suh et al., 2012). The performance measurement indicators were the root mean square error (RMSE), correlation coefficient (R), and model bias (PBIAS). The period from 1976 to 1995 was selected as the calibration period with 1996–2005 as the validation period. The normalised weight and spatially averaged bias for each model were calculated for the training (calibration) period and used to obtain the climate ensemble. Next, using the same weights and spatially averaged biases, the climate ensemble was calculated to obtain the prediction (validation) period to validate performance. The extent of agreement between the multimodel climate ensemble and observed data was used as a criterion for assessing the acceptability of the ensemble method.

Table 1 Selected RCMs for projecting future climate in the Chindwin River Basin under climate change scenarios. RCM

CCAM CCAM CCAM CCAM

(ACCESS) (CNRM) (CCSM) (MPI)

RCM Description

Driving GCM

Spatial Resolution (km)

Commonwealth Scientific and Industrial Research Organisation (CSIRO), Conformal-Cubic Atmospheric Model (CCAM; McGregor and Dix, 2001)

ACCESS1.0 CNRM-CM5 CCSM4 MPI-ESM-LR

50 50 50 50

method was chosen, which is a rather simple technique (Teutschbein and Seibert, 2012; Shrestha et al., 2017). The linear scaling method uses a correction factor for each month based on the long-term historical RCM data and the observed data. Precipitation is corrected with a multiplier, which is the ratio between the mean monthly observed and simulated data for the historical run (see Eqs. (1) and (2)). Whereas the temperature is corrected with an additive term which is the difference between mean monthly observed and simulated data for the historical run as given in Eqs. (3) and (4) below (Teutschbein and Seibert, 2012; Luo et al., 2018). corr Phist , m, d = Phist , m, d corr P fut , m, d = Pfut , m, d

µ (Pobs, m)/ µ (Phst , m) µ (Pobs, m)/µ (Phst , m)

(1) (2)

corr Thst , m, d = Thst , m, d + [µ (Tobs, m )

µ (Thst , m)]

(3)

corr T fut , m, d = Tfut , m, d + [µ (Tobs, m )

µ (Thst , m)]

(4)

3.2. Assessment of climate change impact on hydrology A hydrological model was developed for the Chindwin River Basin using MIKE 11 NAM (Nedbor Afstromnings Model), a software program developed by the Danish Hydraulic Institute (DHI). This model is a deterministic, lumped, conceptual model that can account for water

corr corr where Phist , m, d and P fut , m, d are the corrected precipitation in respectively the historical run and the future forecast on the dth day of the mth

Fig. 3. NAM model structure. 4

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as shown in Fig. 1. Although there was a consistent record of rainfall and discharge, evaporation data was not consistent. Therefore, the calibration and validation periods were selected with due consideration to the availability of continuous evaporation data. Thus, the period from 1991 to 1999 was selected for calibration with 2006–2008 for validation. Tmax and Tmin were used to fill the missing evaporation data by establishing their relationship with evaporation using the multiple regression technique. The same relationship was employed to obtain future evaporation using future temperature data. The model was calibrated using the auto-calibration technique available in MIKE NAM. The calibration criterion involved matching the modelled discharge with the observed discharge at Hkamti, Homalin, Kalywa, and Monywa by minimising the overall water balance error, the overall RMSE, Peak RMSE, and low RMSE with a maximum of 2000 iteration. In addition to the nine default parameters, two more parameters were used to describe the physical properties of the sub-basins, as described in Table 2. For a better description of the baseflow, an extended groundwater component was included. This allows for an additional lower groundwater storage whose recharge is Cqlow which is a proportion of the total recharge G and routed through a linear reservoir with time constant Cklow. to improve the agreement of simulated low flows with the observed data. A sensitivity analysis of the parameters was conducted to find the most sensitive for significantly reducing error, measured by R2, Efficiency Index (EI), and RMSE. The model was run multiple times by adjusting individual parameters (giving attention to the most sensitive) and specifications in the extended groundwater component until the modelled hydrograph achieved the best match with the observed. The model thus obtained was further validated for the period from 2006 to 2008. The calibrated and validated model was used to simulate future runoff from the basin using the results of a multimodel ensemble for future climate under RCP4.5 and RCP8.5.

Table 2 Model parameters used in MIKE 11 NAM. Parameter/ unit

Description

Umax/mm Lmax/mm CQOF CKIF/hr CK1,2/hr TOF TIF TG

The maximum water content in the surface storage The maximum water content in root zone storage Overland flow runoff coefficient The time constant for routing interflow The time constant for routing overland flow Root zone threshold value for overland flow Root zone threshold value for interflow Root zone threshold value for groundwater recharge Time constant for routing base flow Lower base flow - recharge to lower reservoir Time constant for routing lower baseflow

CKBF/hr Cqlow/% of total recharge Cklow/hr

balance in up to four different mutually inter-related water storage zones, namely: surface storage, lower or root zone storage, groundwater storage and snow storage. By default, NAM will use the first three storages with nine parameters to describe the physical properties of these three zones. It is possible to select more parameters for calibration as required (Hafezparast et al., 2013; Danish Hydraulic Institute [DHI], 2003). The general structure of the model and the interactions among different storages is given in Fig. 3 below. The basic modelling components within different storages and their relationships are described below. Overland flow QOF is given by Eq. (5). Flow routing is based on the linear reservoir concept

QOF = CQOF

L / Lmax TOF PN forL / Lmax > TOFandQOF 1 TOF

= 0forL/ Lmax

(5)

TOF

where; L is the lower zone or root zone storage CQOF is the overland flow runoff coefficient (0 ≤ CQOF ≤ 1) TOF is the threshold value for overland flow (0 ≤ TOF ≤ 1) PN is the excess rainfall

3.3. Assessment of the climate change impact on river morphology 3.3.1. Selection of study area for the assessment of morphological changes MIKE 21 model was selected to develop the morphology model. It is a user-friendly two-dimensional model with advanced pre and post processing tools, developed and maintained by the DHI (Paliwal and Patra, 2011). The model comprises of different modules to simulate hydrodynamic, advection-dispersion, sediment transport, water quality, heavy metals etc. It has a wide range of engineering applications in, inter-alia, coastal hydraulics, wave dynamics, environmental investigations, sediment processes, river hydraulics and oceanography (Warren and Bach, 1992; Paliwal and Patra, 2011; Chubarenko and Tchepikova, 2001). Since 2D modelling demands high computational capacity, it is advisable to limit the computational domain in studies of this nature to concentrate on the critical areas only. The downstream of the Chindwin River is densely populated and economically active. Therefore, the study area was narrowed down to focus on the downstream portion. Google Earth satellite images from 2001 to 2017 reveal that the river stretching downstream from Monywa is heavily braided, changing course over the years. Therefore, the area outlined in purple in Fig. 4 was selected for morphological projection. Due to the very limited data available for model development (calibration), the model domain also had to be extended into part of Ayeyarwady River (represented as the domain of the morphology model in Fig. 4). The MIKE 21 Flow Model Flexible Mesh was chosen to develop the river morphology model for the baseline period from 2014 to 2016. A complete river morphology model includes both bed level and river bank changes. Since the coupling of a Hydrodynamic Model (HD) and Sediment Transport (ST) could achieve the above, such a combination was considered as the river morphology model in this study. In the MIKE 21 Flow Model (FM), two models are interconnected through

The interflow QIF is given by Eq. (6) below. It is routed through two linear reservoirs in series with the same time constant CK12.

QIF = (CKIF )

1 L /Lmax

1

= 0forL/ Lmax

TIF UforL / Lmax > TIFandQIF TIF (6)

TIF

where, U is the surface storage CKIF is the time constant for interflow and TIF is the root zone threshold value for interflow (0 ≤ TIF ≤ 1) The part of infiltration recharging the groundwater (G) storage depends on the soil moisture content in the root zone and is represented by the Eq. (7).

G = (PN

QOF )

L /Lmax TG forL / Lmax > TGand0forL / Lmax 1 TG

TG (7)

Where; TG is the root zone threshold value for groundwater recharge (0 ≤ TG ≤ 1). From the groundwater storage the amount contributing the baseflow (BF) is calculated as the outflow from a linear reservoir with the time constant CKBF. As the river basin covers a large area and MIKE NAM is a lumped model, the basin needed to be separated into six sub-basins (SB) to better account for the spatial variation of different physical parameters 5

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Fig. 4. Domain of the morphology model for the baseline setup and future simulation.

morphological simulation where the hydrodynamic flow field is continuously updated to account for changes in bed bathymetry resulting from sand transport. A riverbank erosion model appears as part of the sand transport model. Owing to the limitations of data and high computational time, the calibration period was restricted to only two years.

MIKE 21. It is based on the numerical solutions of 2D Saint Venant equations (depth-integrated incompressible Reynolds averaged NavierStokes equations). The 2D governing equations in cartesian coordinates is given from Eq. (8) through (12) below, which are obtained by integration of horizontal continuity and momentum equations over the depth h = η + d (Warren and Bach, 1992). Continuity equation

3.3.2. Mesh generation for the 2D river morphology model A flexible mesh permits the effective representation of complex geometry in the modelling domain (Mackay et al., 2015). The MIKE Zero package was used to generate the mesh for both HD and ST models. A bathymetric survey for the selected model domain was carried out in 2014 (Fig. 4) and this data was used to generate the mesh coupled with bathymetry data. The domain area is 2550 km2 of which around 693 km2 is in the Chindwin River Basin. Since this 2D model has a significantly large domain and extensive simulation periods, a limit had to be applied to the maximum element area to achieve an affordable model computation time.

h hu¯ hv¯ + + = hS t x y

(8)

Horizontal momentum equations for the x and y components respectively is given by Eqs. (5) and (6).

hu¯ hu¯ 2 hvu ¯ + + t x y = fvh ¯

gh

x

h pa 0 x

gh2 p + 2 0 x

sx

bx

1

0

0

0

sxy sxx + + (hTxx ) + (hTxy ) + hus S x y x y

3.3.3. Hydrodynamic modelling To provide the hydrodynamic basis for morphology simulation, it was necessary to develop an HD model for this study. The HD module in MIKE 21 provides the hydrodynamic basis for all other modules in 6

(9)

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sediment transportation varies. Since this study is a river application the model type is pure current. The sediment transport model is extended to include a helical flow module in order to simulate secondary flows. This module is important for this research since the selected part of the river shows meandering. MIKE 21 FM offers four types of formulas for sediment transport calculation as mentioned below.

hv¯ hv¯2 hvu ¯ + + t y x = fuh ¯

gh

s yx x

+

x s yy y

h pa 0 y +

x

gh2 p + 2 0 y

(hTxy ) +

y

sy

by

1

0

0

0

(hTyy ) + hvs S

(10)

where;

(a) Engelund and Hansen (total transport formula) (b) Van Rijn (distinguish between bed load and suspended load) (c) Engelund and Fredsoe (distinguish between bed load and suspended load) (d) Meyer-Peter and Muller (only bed load)

t – time x, y – cartesian coordinated η – surface elevation d – still water depth h – total water depth u, v – velocity components in x and y directions respectively f – Coriolis parameter g, ρ – gravitational acceleration and density of water sxx, sxy, syx, syy – radiational stress tensor ρ0 – reference density of water S – magnitude of discharge due to point sources us, vs – velocity by which water is discharged into ambient water and overbar indicated the depth averaged values. Eg. u¯,v¯ defined by,

hu¯ =

d

udzandhv¯ =

d

vdz

Van Rijn model is used in this study. He proposed the following model described by Eqs. (14) through (18) for bed load and suspended sediment load transport.

S = 0.053

Tij – lateral stress (viscous friction, turbulent friction and differential advection) estimated using depth averaged velocity gradients.

Txx = 2A

u¯ , Txy = A x

u¯ v¯ v¯ + , Tyy = 2A y x y

UfÍ = V D

(12)

The spatial discretization of the equations is conducted using cell centered finite volume method. The spatial domain is discretized into non-overlapping element/cells. These elements are either triangles or quadrilateral elements in an unstructured grid in the horizontal plane. This module uses an explicit scheme for time integration. (DHI, 2016a). The turbulance is modelled using eddy viscosity models. In the 2D model only the horizontal turbulance is modelled and the available options are constant constant eddy formulation or Smagorisky formulation. Smagorinsky proposed a subgrid scale eddy viscosity which relates to a characteristic length scale as given in equation below.

Uj 1 Ui + (i, j = 1, 2) 2 Xj Xi

c (s

(15) (16)

1) gd50

g (17)

C'

= d50

(s

1) g (18)

2

T - non-dimensional transport stage parameter Ufc – critical friction velocity Uf′ - effective friction velocity C′ - Chezy number D* - non dimentional particle diameter υ – Kinematic viscosity In this formulation, instead of a constant critical Shields parameter, a one that varies with D* is assumed. As discussed earlier, a morphological model as identified by MIKE 21 Flow Model is a combination of hydrodynamic and sediment transport models. Therefore, in this combined model the hydrodynamic flow field is updated continuously according to modifications in bed bathymetry. The rate of bed level change in the morphology model is obtained based on the Exner equations or sediment continuity equation given as Eq. (19).

A = Cs2 I 2 2Sij Sij

Sij =

(14)

1

Ufc

Ufc =

3 1) g. d50

2

U f'

T=

(11)

T 2.1 (s D 0.3

(13)

Here, Cs is a constant which is calibrated in the model, l is the characteristic length and Sij is the deformation rate. The model allows a range for minimum and maximum eddy Daily discharge and water level were provided respectively for upstream and downstream boundaries, with initial conditions (water surface elevation) as constant over the model domain. This constant value was approximated as the average for boundaries at the beginning of the simulation. Bed resistance and eddy viscosity are the parameters needed to be calibrated in the hydrodynamic module. Water depth at Pakokku and discharge at Nyaung-U were used for model calibration (Fig. 4).

1(1

n)

Sy z S = x + t x y

S

(19)

where n – bed porosity, z- bed level, t – time, Sx, Sy – bed load/ total load transport in x and y directions, ΔS – sediment sink source rate. In an equilibrium situation of sediment transport, the ΔS term is zero unless lateral sediment supply occurs in the system. An example for such a situation is bank erosion, which will also be modelled in this study. In a non-equilibrium situation (requiring solution of an advection dispersion equation for a suspended load) Eq. (20) below is used for ΔS term. Here the sediments will deposit on bed if the sediment concentration in water is greater than the equilibrium concentration and vice versa. If in the selected study area, the suspended sediment transport is significant, the non-equilibrium conditions will be chosen. Moreover, if significant flow curvature is present the helical flow will also be included.

3.3.4. Sediment transport (ST) modelling Sand Transport (ST) Module of Mike21 Flow Model FM enables modelling of erosion, transport and deposition of sand under existing flow condition. The module calculates the sediment transport capacity of each cell for a given bathymetry, hydrodynamic and sediment conditions (Warren and Bach, 1992). Depending on whether the sediment transportation is occurring under pure current or combined wave and current conditions, the laws and equations of hydrodynamics and

S = Ф0 ( 0) ws (c 7

ce )

(20)

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η0 - normalized no slip level above the bed Ф0 – unit profile function for the sediment concentration Ws – settling velocity for suspended sediments c – depth averaged sediment concentration ce – depth averaged equilibrium concentration

the Chindwin River. Thus, the model domain was limited to a stretch between the upstream boundary (at Monywa) and downstream boundary at the catchment outlet (near the Chindwin-Ayeyarwady confluence). This allowed the mesh to be reformulated into a finer resolution, compensated by the reduced domain area. Thus, the obtained model could be used to project changes in the future river morphology (with respect to 2014) using future hydrological data (future discharge at Monywa and at the catchment outlet) obtained from the calibrated and validated MIKE NAM model. During simulation of the morphology model for the baseline period, the discharge and water level at the Chindwin River Basin outlet (downstream boundary) was obtained to develop a stage-discharge relationship at this location. Future discharge at the same location was obtained by the MIKE NAM model. The corresponding water level at the Chindwin outlet (as well as the downstream boundary condition of the future morphology model), was obtained using the rating curve (stagedischarge relationship) developed earlier. The simulation is performed only for the three most extreme rainfall events in the near future under RCP4.5 and RCP8.5. Therefore, future morphological change is relative to the morphology of 2014. Only a comparison of changes caused by each event can be performed and not absolute morphology. This kind of event-based simulation is the most common type in morphology-related assessment due to high computational demand and problems in relation to stability for long period dynamic simulations. The morphology model was simulated for very high rainfall events that cause severe floods, and low flow events. It was decided to refer any rainfall event exceeding the 95th percentile value of the annual maximum rainfall distribution as a very high rainfall event. Low flow events were selected on the basis of the lowest annual flow during the hot and dry season.

Therefore, through a morphological simulation, the river bed is updated at every time step in the HD model. Modelling of river bank erosion is the final part of the river morphology modelling. River bank erosion model, is in fact a part of the ST model which is designed to calculate slope failure. The above mentioned slope failure could be either a simple bank erosion, extended bank erosion or a general slope failure depending on the definition of erodible elements. The general slope failure mode was selected and the calculation is such that the model uses a user defined angle of repose (constant or possible to include spatial variation) to activate the sloping failure. In modelling bank slope failure, the bank slope should be calculated at all adjacent elements covering transition from flood to dry. Bank slope α can be defined by the following Eq. (21).

tan =

zd

zf (21)

ds

If α is greater than the angle of repose Ò¨ the bank slides and adjusts to the slope Ò¨ . If this occurs the bed level of the concerned elements should be modified as described by the Eq. (22) below.

If tan > tanÒ¨ then

zd = zd zf = zf

Af

(z d

zf

tan(Ò¨) ds

Ad (z Af + Ad d

zf

tan(Ò¨) ds

Af + Ad

(22)

where Zd, Zf bed levels at dry and flooded elements respectively. Af and Ad are the areas of flooded (wet) and dry elements respectively and ds is the distance between two cell centers (DHI, 2016b). The required input data consists of sediment concentrations for boundary conditions and some sediment properties such as porosity, grain diameter, and relative density of sediments. Unfortunately, of the three boundaries, sediment transport data is only available at Sagain. Therefore, zero gradients of sediment concentration were assigned to stations Monywa and Nyaung-U. The sediment properties of the Chindwin River have not been previously tested. Therefore, riverbed and bank sediment samples were obtained and tested in a lab for porosity, particle distribution, and angle of repose. The horizontal dispersion was modelled using scaled eddy viscosity formulation since this is the recommended option when sophisticated models such as the Smagorinsky are used (DHI, 2016b). For the combined HD and ST models, the only parameters requiring calibration are bed resistance and the Smagorinsky constant. Since morpho-dynamics is the representation of the response of bathymetry to hydrodynamic processes, in morpho-dynamic models an accurate representation of related hydrodynamics is crucial. Validation of the model’s ability to simulate hydrodynamic process as such is an indication of the model’s overall reliability. The observation records for suspended sediment concentration (SSC) to validate the sediment transport model were not available. Therefore, the combined HD and ST model was validated using the observed water depth at Pakokku and observed discharge at Nyaung-U. The objective of this aspect of the research was to predict the morpho-dynamic change associated with bed level change. The agreement of simulated water depth with observed water depth suggests that the bed level changes (due to sediment erosion/deposition) have been accurately modelled.

4. Results and discussion 4.1. Climate change in the Chindwin river basin 4.1.1. Performance of linear scaling and the multimodal ensemble method in climate prediction All the model performances (raw RCM, bias-corrected RCM, and multimodel ensemble for the training and prediction periods) were compared against the respective observed data at each location, using two standard error parameters: R2 and RMSE. The results obtained are shown in Figs. A.1–A.3 in the supporting information. The multimodel outperformed the individual RCMs for both rainfall and temperature, justifying the use of a multimodel ensemble for future analysis of climate, hydrology, and eventually, morphology. Deriving climate projections at a location of our interest from RCMs under different emission scenarios is a step-wise process where uncertainties accumulate in its every step. In an attempt to reduce such uncertainties and improve the robustness of the climate projections, this study has used data from multiple climate models, bias corrected them and obtained a multimodal ensemble of them. This has been helpful in addressing uncertainty to a satisfactory extent but not completely eliminating it. 4.1.2. Projected changes in rainfall and temperature in near future A comparison of annual rainfall during the baseline and near future in the Chindwin River Basin (Fig. A.4) shows that of the five stations, Hkamti and Monywa have the highest (about 3,400 mm) and lowest (500 mm) rainfall variability (max–min range), respectively which is found to decrease in future. Other stations show similar rainfall variability to each other during the baseline period, which is projected to increase in Homalin and Mawlaik while decreasing at Kalewa, in the future. There is a general increase in mean annual rainfall, however, a clear change in pattern cannot be observed due to significant interannual variability. The maximum rainfall is projected to increase in the

3.3.5. Simulation of morphological changes in the near future The morphology change in the selected part of the river was simulated under future climatic conditions to assess the impact of climate change. The entire model setup including all parameters was kept constant for the future with the model domain reduced to focus only on 8

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Table 3 Percentage change of monthly rainfall in the near future (2018–2040) compared to the baseline (1975–2005).

near future, except at Homalin and Monywa, and this can have a significant impact on the river morphology. Table 3 shows the percentage change of average monthly rainfall in the near future compared to the baseline period. The results generally reveal that in the near future, wet months (May, June, July, and September) will receive more rainfall while the dry months (November, December January, and February) will receive less. However, the monsoon months (August and October) are projected to receive less rainfall in the near future. Unlike rainfall, temperature projection shows a clear increasing pattern. Tmax shows a positive anomaly more than 75% of the time in the near future at all locations under both scenarios. Highest (2.5 °C) and lowest (0.84 °C) annual mean changes (positive anomaly) are projected for Hkamti and Monywa, respectively, under RCP4.5. However, under RCP 8.5, the highest positive anomaly is projected for Monywa and the lowest at Homalin (refer to Fig. A.5 in the supporting information). Tmin also shows a clear increasing pattern compared to the average baseline value at all locations under both scenarios in almost all near future years with inter-annual variability in absolute change lying between 1 and 1.6 °C (refer to Fig. A.6 in the supporting information). The highest increase in Tmin under RCP 4.5 is projected for Hkamti station (1.5 °C) while the lowest is at Monywa (0.6 °C). Under RCP 8.5 the highest increase is at Monywa (1 °C) while the lowest is at Mawlaik (0.7 °C).

period, while not all the peak flows have been predicted well. The negative water balance error indicates that the model is underpredicting the discharge at Monywa (the station closest to the outlet of the basin), although the R2 and EI values are acceptable. Overall, the optimised model parameter values from the calibrated model are acceptable for satisfactory simulation of future discharge at Monywa. The calibrated parameter values for each sub-basin are shown in Table 5. 4.2.2. Projected hydrology at Monywa station in the near future The subsequent discussion focuses on the discharge at Monywa station since it is closest to the outlet of the basin. The projected near future annual discharge at Monywa is shown in Fig. 6 (a) in the form of a box and whiskers plot diagram and detailed further in Table A.1 in the supporting information. The variability of annual discharge is projected to increase under RCP8.5 and decrease under RCP4.5. Moreover, in the near future, annual discharge is higher than the mean value during the baseline under both scenarios more than 50% of the time. However, the mean annual discharge is projected to increase by 7.9% and 17.7% compared to the baseline value in the near future under RCP4.5 and RCP8.5, respectively. When the baseline and near future annual hydrographs shown in Fig. 6(b) are compared, the discharge during October, November, and December is shown to decrease under both scenarios. The annual monsoon flood will have multiple and amplified peaks in the near future compared to the single peak during the baseline period. The onset of monsoon will also be advanced by about eight to ten days. In addition, the rise and recession of the flood hydrograph will be faster in the future (steeper rising and recession limbs) compared to the baseline period. Adding further to the study of discharge at Monywa, a seasonal analysis was also carried out. Fig. 7 shows the behaviour of daily discharge during the baseline period and near future in the three typical climate seasons. Discharge in the hot and dry season (Fig. 7(a)) is projected to increase compared to that of the mean baseline 75% of the time in the near future under both scenarios, and the positive anomaly is predominant under RCP8.5. Maximum daily discharge (upper extreme) in this season (1461 m3/s under RCP4.5 and 1666 m3/s under RCP8.5) is projected to increase compared to the baseline period (1264 m3/s) in the near future under both scenarios. The mean discharge is projected to be 19.9 and 38.4% higher compared to the baseline under RCP4.5 and RCP8.5, respectively. During the wet season, which makes the highest contribution to annual discharge, the mean discharge is projected to increase in the near future under RCP4.5 (about 10.3%) and RCP8.5 (about 19.3%) (Fig. 7(b)). Fig. 7(c) further indicates that in the cold and dry season, the mean discharge is projected to decrease by 15.5 and 1.9%, respectively under

4.2. Impact of climate change on hydrology in the Chindwin river basin 4.2.1. Calibration and validation of the hydrology model Hydrological models accumulate uncertainties at every step of the modelling process. They arise individually and collectively from input data (rainfall, temperature etc. from both observed and forecasted climate), model parameters and state variables. While an appreciable amount of efforts have been made to address uncertainty in hydrological predictions, it remains a challenging area where the topic itself is far from full comprehension (Liu and Gupta, 2007). However, this study has given attention to reduce uncertainty and improve the reliability of hydrological simulations by calibrating (following a sensitivity analysis) and validating the model, at multiple locations for a reasonably long time period under the limitations of observed data. While there exist advanced and time-consuming methods to reduce model uncertainty further, they were considered as beyond the scope of this study. The agreement between observed and simulated discharge in calibration and validation periods was tested with R2, Efficiency Index (EI) and Water Balance Error (Table 4). Fig. 5 indicates that low flows are generally captured well during both the calibration and validation 9

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Table 4 Performance of the MIKE NAM model in the Chindwin River Basin. Station

Hkamti Homalin Kalewa Monywa

R2

EI

Water Balance Error (%)

Calibration

Validation

Calibration

Validation

Calibration

Validation

0.88 0.84 0.89 0.88

0.89 0.89 0.82 0.85

0.88 0.81 0.78 0.87

0.86 0.87 0.82 0.81

0 −16.2 −11 −2.8

−15 11.1 −0.5 −14.8

Fig. 5. Observed vs. simulated hydrographs for calibration (top) and validation (bottom) periods at Monywa (near the outlet of the Chindwin River Basin). Table 5 Parameter values resulting from the calibrated MIKE NAM model developed for the Chindwin River Basin. Parameter/unit

Umax/mm Lmax/mm CQOF CKIF/hr CK1,2/hr TOF TIF TG CKBF/hr Cqlow/% of total recharge Cklow/hr

Parameter Range

10–40 50–400 0.1–1 200–1000 10–50 0–0.99 0–0.99 0–0.99 100–4000 0–100 1000–40000

Calibrated Value SB1

SB2

SB3

SB4

SB5

28.3 591 0.102 206.6 48.6 0.0995 0.298 0.029 373 – –

10.7 585 0.155 534 28.1 0.981 0.79 0.115 1783 97.2 39442.2

32.9 559 0.366 524.2 39.1 0.934 0.96 0.025 3407 99.6 39312.5

27.6 567 0.387 403.8 34.6 0.985 0.983 0.256 2726 99.5 39410.6

27.2 549 0.728 720 10.6 0.959 0.953 0.897 1007 0 35441.1

RCP4.5 and RCP8.5. Under RCP4.5, there is a clear decrease in projected discharge, with daily discharge in this season being lower than that of the mean baseline more than 75% of the time in the near future. Whereas under RCP8.5 greater variability for this season is projected. Near future discharge variability is projected to decrease under RCP4.5 and increase under RCP8.5 for all three seasons.

Finally, to analyse the future discharge pattern at Monywa, a flood frequency analysis for the annual maximum series was conducted using the Weibull formula and flow duration curves (FDC) were plotted for the daily time series. The results for the FDC in Fig. 8(a) show that, under RCP8.5, discharges with a 40% exceedance probability and above are not projected to change significantly, whereas discharges 10

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Fig. 6. Annual discharge (a) and annual flood discharge (b) at Monywa during the baseline period (1975–2005) and near future (2018–2040) under RCP4.5 and RCP8.5.

with an exceedance probability below 40% will increase. Accordingly, the number of days with high flows will increase in the future while no significant change is expected for low flows. Furthermore, the annual average increase discussed earlier (Fig. 6) will mostly enhance high flows over low flows. Under RCP4.5, discharges with an exceedance probability higher than 34% and less than 8% are not projected to change significantly, whereas discharges with an exceedance probability of between 8% and 34% will increase. Therefore, the annual average increase projected under this scenario will mostly contribute to increasing mid-range flows, while high flows and low flows will not significantly increase (see flow duration curve in Fig. 8(a)). The flood frequency curve (FFC) (Fig. 8(b)) indicates that extreme flood events are becoming more severe in the future under both scenarios, in terms of both magnitude and frequency, which is further complemented by the FDC. For example, the FFC shows that floods with a return period of 10 years will have a higher magnitude under both scenarios in the near future (more severe under RCP 8.5). An example of the increased flood frequency can be made by observing the FDC. For a flood event discharging 15000 m3/s, the probability of exceedance during the baseline, near future RCP4.5, and near future RCP8.5 are 5.5, 6, and 9.6%, respectively. 4.3. River morphology changes induced by extreme climate events under climate change scenarios

Fig. 7. Seasonal discharge during the hot and dry (a), wet (b), and cold and dry (c) seasons at Monywa during the baseline period and near future.

4.3.1. Calibration of the morphology (combined hydrodynamic and sediment transport) model The study has calibrated the model at two locations to reduce uncertainty and improve the robustness of the model results under the 11

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Fig. 8. Flow duration curve (a) and flood frequency curve (b) at the Monywa station during the baseline period (1975–2005) and near future (2018–2040).

Fig. 9. Boundary conditions for the extreme flood event in 2028 (left) and extreme low flow event in 2018 (right).

limitations of data availability. However, it should be noted that had there being a time series of observed sediment data within the model domain, the uncertainties of the model in simulating bed level change could have been further reduced by calibrating the model against such data.

After numerous trials, for the calibrated model, bed resistance in terms of Manning’s number was obtained as 50 m1/3s−1 with the scaled Smagorisky constant at 0.28. A comparison of observed vs. simulated water depth at Pakokku returned the coefficient of determination (R2) as 0.996, EI as 0.989 and Root Mean Square Error (RMSE) as 0.32 m. 12

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near future years. Boundary conditions for the extreme flood event and the extremely low flow event discussed above are presented in Fig. 9. This analysis consists of plotting the initial and final cross-section profiles of the riverbed under the flow events. The initial profile is the same for both events as extracted from the 2014 surveyed cross-section data. Therefore, changes in the same initial cross-section profile under different flow events will be discussed. The geographical location of the plotted cross-sections is shown on the map in Fig. 10. The set of cross-section profiles plots (shown in Fig. 11) for the extreme flood event in 2028 reveal that the following morphological changes can be expected from an extremely high flow event: A high morphological change, in terms of sedimentation, can be expected in part of the reach at Monywa, upstream areas of Salingyi (CS 1 and 2), and mid-areas of Myaung and Yesagyo Townships (CS 16 and 17). In the upstream region, a channel migration towards the Monywa Township is projected at CS1. The right bank in the upstream is steeper and higher in elevation than the left, causing inundation of the left bank townships (Monywa) during flood events such as that analysed in this study during 2028. However, more than 3.5 km downstream of CS1, and near to CS2, the channel braiding pattern is projected to be caused by the flood and the resulting inundation worsened by high sediment deposition in the middle of the reach. The upper regions of Chaung U and lower regions of Salingyi are less affected in terms of morphological change by the selected extreme flood event. The upper regions in Yesagyo and lower regions in Chaung U (CS10 to 12) are also projected to experience morphological change in terms of channel narrowing and raising of the channel bed. Therefore, these areas showing bed level change due to sediment deposition will result in high flood levels overflowing the bank due to a reduction in the flow area of the main channel. The downstream part of the river reach has wider cross-sections and milder slopes and consequently considerable inundation will be caused by the flood. However, these cannot be explained with absolute values, since all the aforementioned projected changes are relative to the 2014 bathymetry data. Moreover, the model does not seem to simulate the erosion process with much accuracy, and this can be mainly attributed to the inadequacy of spatially distributed data for describing suspended, bed, and bank sediment properties (e.g., angle of repose, porosity etc). Moreover, the model domain is relatively large (due to data limitations) compared to other morphological model applications found in the literature. As previously discussed, the annual hydrographs developed for the near future will have multiple flood peaks under both scenarios, in contrast to the baseline period which had a single peak. When the time series for bed level change at a given cross-section was observed, a significant deposition or upward bed level change was observed after the first peak. Fig. 12 explains the bed level changes at three points of time in the simulated time series: a) after the first peak; b) after the second peak; and c) after the third peak. Therefore, this particular change, associated with multiple peaks in hydrology in the near future under climate change scenarios will have a significant impact in terms of morpho-dynamic changes. In comparison to the high flow event of 2028, the cross-sections show that hardly any morphological change has resulted in low flows. Therefore, it can be confirmed that sedimentation and river morphological changes are mainly due to high flood events. This observation further supports the theory that climate change can aggravate the problems due to changes in river morphology since high flood events are predicted to increase in magnitude and frequency in the near future. The results and the model can be used to formulate both mitigation strategies and adaptation measures. Channel narrowing and raising of the channel bed are shown to contribute to the severity of floods, and river managers may invest in river training measures for the vulnerable

Fig. 10. Location of the river cross-sections considered for assessing the morphological changes from extreme events in the near future (2019–2040).

This assessment of statistical parameters indicates that the performance of the model in the morpho-dynamic simulation is satisfactory. Figure A-7 in the supporting information provides further calibration results and a comparison of observed vs. simulated water depth at Pakokku and discharge at Nyaung-U during the calibration period. 4.3.2. Morphological changes resulting from extreme events under climate change scenarios Morphological changes resulting from high flow (and rainfall) and low flow extreme events are discussed in this section. A flood event resulting from rainfall exceeding the 95th percentile value of the annual maximum distribution was selected as the extreme high flow event. The period considered for the morphology change simulation was 2019–2040. Only two rainfall events exceeded the 95th percentile value of the annual maximum rainfall distribution, under both RCPs. The rainfall values were 227 mm and 204 mm under RCP4.5 and RCP8.5, respectively. These two events caused multiple flood peaks as predicted by the hydrological analysis. The most severe event of the two (higher flood magnitude) projected for 2028 under RCP8.5, was selected to study the morphological change caused by the flood since both events show similar morpho-dynamic responses. The simulation period for the selected event was from 01/07/2028–12/11/2028. In addition, a low flow extreme event projected to occur in 2018 under RCP4.5 was also simulated. The simulation period for this low flow event was 15/01/2019–10/04/2019. The event was selected on the basis of lowest average discharge during the hot and dry season (which has the lowest average daily discharge in a typical year) in all

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Fig. 11. Bed level at the beginning (initial) and end (final) of the extreme monsoon flood event in 2028 at different locations in the Chindwin River downstream of Monywa.

sections to minimise morphological changes. The impacts of such measures can be compared by running the model after incorporating the changes to the river section to enable optimum solutions to be found. If necessary, a physical model can be used to verify the mathematical model results. Catchment conservation is another intervention that would reduce sediment loads. The model can also be used to predict the impact of reducing sediment loads. Such an approach to mitigating the impact of flood will minimise the cost of physical intervention. In addition, adaptation measures such as the evacuation of people in vulnerable areas during flood events, demarcating vulnerable areas and adopting development restrictions, and early flood warnings are some of the adaptation measures which can be formulated using the model.

from extreme climate events under climate change scenarios and their socio-economic significance. The results obtained from RCMs indicate that the mean annual rainfall will generally increase in the Chindwin River Basin in the near future. However, there is no clear trend due to the increased inter-annual variability of the projected rainfall. The calibrated hydrological model developed using MIKE NAM made a reasonably accurate representation of the Chindwin River Basin rainfall-runoff process. Rise and recession of the hydrograph will be faster in the future. The projected hydrograph shows multiple peaks instead of the single peak in the baseline period. During the hot and dry season and wet season, the mean and median discharge will increase in the near future under both RCPs, while decreasing in the cold and dry season. A flood frequency analysis reveals that extreme discharge events will be higher in magnitude and frequency in the near future under both climate scenarios. The sedimentation and change in river morphology are mainly due to high flows, which are expected to increase in frequency and magnitude under previously described climate change scenarios. An

5. Conclusions This study was conducted to analyse the hydrological and morphological changes in the Chindwin River in the near future resulting 14

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Fig. 11. (continued)

analysis of morphological change indicates that flows following the first flood peak account for most of the sediment deposition, concluding that anticipated multiple peaks in the hydrograph in the near future will exacerbate floods and morphological changes. Inundation of riverine villages, shifting of channel paths or more braiding can be expected due to sediment deposition at several locations. This will result in problems with navigation, damage to social and economic infrastructure as well as agricultural lands, all of which pose a threat to associated ecosystem habitats, and adversely affect river-associated lives and livelihoods. Better results for river bank erosion could be obtained if data availability was improved. Structural solutions to these problems can be incorporated into the model to simulate different mitigation and adaptation options. Therefore, the results of this study could be used to facilitate well-informed decision making, leading to meaningful actions and better preparations for the challenges of climate change. Major limitations of the study include a lack of data and

information, thus inhibiting proper representation of spatial and temporal morphological changes. Therefore, future studies should improve the reliability of the model by measuring the hydrodynamic (velocity) and sediment discharge data, investigating sediment properties with spatial variations, collecting data from higher resolution bathymetry surveys, and including such data in the model. Furthermore, it is recommended that the model be continuously updated to account for climate and land use changes and a rolling plan be developed to adapt and extend the entire assessment to study the mid and far future challenges associated with morphological changes. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Fig. 12. Bed level change in the Chindwin River after (a) the first peak of 22,266 m3/s, (b) second peak of 29,058 m3/s in the middle, and (c) the third peak of 23,090 m3/s for the extreme monsoon flood event in 2028.

Acknowledgements

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The authors would like to thank the Stockholm Environment Institute (SEI) and the Directorate of Water Resources and Improvement of River Systems (DWIR), Myanmar for providing all the required data for this research. The authors would also like to acknowledge the Water Engineering and Management (WEM) Program, Asian Institute of Technology (AIT), Thailand for providing a high-performance computer for model simulation and access to the laboratory for soil testing. Furthermore, we are very grateful to the Danish Hydraulic Institute (DHI), Thailand for providing a licensed version of the MIKE software package. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.catena.2020.104464. References Ashmore, P., Church, M., 2001. The impacts of climate change on rivers and river processes in Canada. Geological Survey of Canada Bulletin, Ottawa, Ontario. Bashir, A., 2013. Changes in channel morphology and its socio-economic impacts on the riverine communities in Yola area. Int. J. Environ. Ecol. 23–30. Bindoff, N.L., Stott, P., AchutaRao, K.M., Allen, M.R., Gillett, N., Gutzler, D., Zhang, X., 2013. Detection and attribution of climate change: from global to regional. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Chang, H.H., 2008. River morphology and river channel changes. Trans. Tianjin Univ. 254–262. Chubarenko, I., Tchepikova, I., 2001. Modelling of man-made contribution to salinity increase into the Vistula Lagoon (Baltic Sea). Ecol. Model. 138 (1–3), 87–100. Danish Hydraulic Iinstitute, 2003. MIKE 11- A modelling system for Rivers and Channels User Guide. DHI, Denmark. Danish Hydraulic Institute, 2016a. MIKE 21 Flow Model FM - Hydrodynamic Module User Guide. DHI, Denmark. Danish Hydraulic Institute, 2016b. MIKE 21 & MIKE 3 Flow Model FM - Sand Transport

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