Sustainability Science
https://doi.org/10.1007/s11625-021-01034-6

SPECIAL FEATURE: ORIGINAL ARTICLE
Synergies and Trade-offs between Sustainable Development Goals and Targets

Large‑scale flood risk assessment under different development
strategies: the Luanhe River Basin in China
Jiaheng Zhao1,2 · Huili Chen1,2 · Qiuhua Liang1,2Fabrice G. Renaud3 · Lee Bosher2 · Xin Zhou6· Xilin Xia1,2 · Jiren Xu3 · Trevor Hoey4 · Brian Barrett5 ·Received: 19 April 2021 / Accepted: 30 August 2021
© The Author(s) 2021

Abstract
Increasing resilience to natural hazards and climate change is critical for achieving many Sustainable Development Goals(SDGs). In recent decades, China has experienced rapid economic development and became the second-largest economy in
the world. This rapid economic expansion has led to large-scale changes in terrestrial (e.g., land use and land cover changes),aquatic (e.g., construction of reservoirs and artificial wetlands) and marine (e.g., land reclamation) environments acrossthe country. Together with climate change, these changes may significantly influence flood risk and, in turn, compromise
SDG achievements. The Luanhe River Basin (LRB) is one of the most afforested basins in North China and has undergone
significant urbanisation and land use change since the 1950s. However, basin-wide flood risk assessment under different
development scenarios has not been considered, although this is critically important to inform policy-making to manage thesynergies and trade-offs between the SDGs and support long-term sustainable development. Using mainly open data, thispaper introduces a new framework for systematically assessing flood risk under different social and economic development
scenarios. A series of model simulations are performed to investigate the flood risk under different land use change scenarios
projected to 2030 to reflect different development strategies. The results are systematically analysed and compared with thebaseline simulation based on the current land use and climate conditions. Further investigations are also provided to consider
the impact of climate change and the construction of dams and reservoirs. The results potentially provide important guidance
to inform future development strategies to maximise the synergies and minimise the trade-offs between various SDGs in LRB.
Keywords Sustainable Development Goals · Flood risk · Climate change · Land use change · Hydrodynamic floodmodelling · Open data

Introduction

Handled by Osamu Saito, Institute for Global Environmental
Strategies, Japan.

To achieve a sustainable future, the UN’s 2030 Agenda for
Sustainable Development (UN 2015) proposed an ambitious plan of action for “People, planet, and prosperity”.
The Agenda sets out 17 Sustainable Development Goals* Huili Chenh.chen2@lboro.ac.uk

3

School of Interdisciplinary Studies, University of Glasgow,
Dumfries, UK* Qiuhua Liangq.liang@lboro.ac.uk

4

Department of Civil and Environmental Engineering, Brunel
University London, London, UK

5

School of Geographical and Earth Sciences, Universityof Glasgow, Glasgow, UK

6

Institute for Global Environmental Strategies (IGES),
Hayama, Japan

1

Institute for Hydroinformatics and Hazard Resilience(IHHR), Hebei University of Engineering, Handan, China

2

School of Architecture, Building and Civil Engineering,
Loughborough University, Loughborough, UK

13Vol.:(0123456789)

Sustainability Science(SDGs) adopted by all member states to action to end poverty, hunger and inequalities and protect the planet, ensuring
all people enjoy peace and prosperity by 2030. The Agenda
recognises that disasters linked to natural hazards could set
back development progress in some countries, especially in
low- and middle-income countries (UN 2015). In this context, disaster risk reduction (DRR) is important to the Sustainable Development Agenda and is primarily driven by the
Sendai Framework for Disaster Risk Reduction 2015–2030(UNISDR 2015). DRR is directly or indirectly incorporated in the SDG framework, and its importance has been
explicitly recognised in many targets, including building theresilience of the poor and reducing their exposure to climaterelated disasters (Target 1.5), ensuring sustainable and resilient food production systems that maintain ecosystems andadapt to climate change (Target 2.3), building sustainable
and resilient infrastructure (Target 9.1), reducing deaths
and impacts from disasters including water-related disasters(Target 11.5), implementing integrated policies for holistic
DRR in link with the Sendai Framework (Target 11.b), andthe targets under Goal 13 of climate action including particularly Target 13.1 on strengthening resilience and adaptive
capacity to climate-related hazards and natural disasters andTarget 13.3 on awareness-raising and capacity building on
climate mitigation, adaptation and early warning. Achieving
these Goals and Targets will impact further on the achievement of other SDGs, particularly SDG 1 (no poverty), SDG
3 (good health and well-being), SDG 5 (gender equality),
SDG 6 (clean water and sanitation), SDG 8 (decent work andeconomic growth), SDG 10 (reducing inequalities) and SDG
15 (protection of ecosystems and biodiversity).
Flooding is one of the most widespread natural hazards
globally, flood-related disasters accounting for 44% of thetotal number of globally recorded natural disasters between
2000 and 2019, 41% of the total affected people, and 22%of the total economic losses, with China being one of themost affected countries (CRED and UNDRR 2020). Floodrisk is increasing due to climate change and more active
human-landscape interaction (IPCC 2014; Hirabayashi et al.
2013; Arnell and Gosling 2016). In particular, the impact ofclimate change on flood risk can be exacerbated by rapid anduncontrolled economic development that leads to increasing
impermeable surface area; population growth and socio-economic development continue to drive rapid land use change
and developments in flood-prone areas, potentially increasing flood exposure and eventually flood risk.
In the past few decades, substantial research has been
undertaken to better understand the impact of climate change
on flood risk from the global to local scales. For example,
on the global scale, Hirabayashi et al. (2013) investigatedflood risk in the 21st century by applying a global river routing model under four different Representative Concentration Pathway (RCP) scenarios, revealing an increase in flood

13

frequency in Southeast Asia, Peninsular India, eastern Africa
and the northern Andes. On the continental or regional scale,
Bevacqua et al. (2019) analysed the concurrence of high sea
level and heavy precipitation events across Europe, showingthat the Mediterranean coasts are currently subject to thehighest probability of compound flooding, and parts of thenorthern European coast may expect a higher probabilityof compound flooding in the future due to climate change.
At a national scale, Miller and Hutchins (2017) provided an
overview of the flood risk in the UK under the impacts ofurbanisation and climate change. At a more localised scale,Bouwer et al. (2010) combined projected future socio-economic change (land use change and increase in the valueof assets), flood scenarios and a simple damage model toinvestigate the variation in future flood risk due to climatechange and development in a Dutch polder area. Most ofthese studies, although at a range of scales, have concluded
that climate change and human activity will change future
flood risk and so will cause greater challenges in developing effective multi-scale flood risk management strategies toensure flood resilience and sustainability.
The adaptive capacity to address flood risk on a local orregional scale is one of the most critical factors for timely
and effective decision-making on developing strategies(Bosher and Chmutina 2017), which is especially germanein the context of dynamically changing socio-economic andclimatic conditions. Scott et al. (2013) argue that any development in a flood-risk area that does not factor in flood risk
reduction and management is not going to be sustainable in
the long term. Likewise, Lizarralde et al. (2015) explain that,while there are challenges to making physical built assets
more sustainable and suitably resilient to hazards such as
floods, there are also exciting opportunities for synergy.
Therefore, scientists and engineers are increasingly being
urged to quantify flood risk under different scenarios so that
more informed decisions can be made about how new developments can be planned, designed and constructed to be
more resilient and sustainable for the long term.
Socio-economic development drives land use and land
cover (LULC) changes, affecting hydrological processes that
may lead to flooding and also determine water resources
and the transport and dilution of pollutants (Wheater andEvans 2009). Based on LULC scenarios representing different socio-economic development strategies, Te Linde et al.
(2011) estimated both current and future (2030) fluvial floodrisk for the whole Rhine basin, summarised that a major partof annual expected damage in the Rhine basin was imposedby climate change. Whilst substantial research has been
undertaken to understand and assess flood risk under different climate change and LULC conditions (e.g., Lugeri
et al. 2010; Akter et al. 2018), most published studies havefocused on high-income nations due to the availability ofhigh-quality data and modelling and assessment tools.

Sustainability Science

Furthermore, flood risk is directly impacted by climatechange and socio-economic development, and so is inevitably an important factor to be considered when implementing the SDGs. Flood risk may significantly affect thelinkages between the SDGs (Baldassarre et al. 2019). There
are apparent research needs, including the development of
fit-for-purpose flood risk assessment frameworks to better
understand how flood risk is related to the synergies andtrade-offs between the SDGs that are influenced by social
development, human–environment interactions, and climatechange. A particular focus should be placed on developing
countries where further research is urgently needed to better
understand the interconnected links between socio-economic
development, climate change and flood risk, and provide
scientific evidence to inform SDG implementation policies
to minimise trade-offs and maximise synergies.
After 40 years of rapid economic development, China
has become the second largest economy in the world but
remains to be the largest developing economy with a percapita GDP of only 1/6th that of the United States. China's
rapid economic development has created noticeable impacts
on the environment, climate and social elements which are
inevitably linked to future flood risk and create different barriers in the realisation of SDGs at different levels. Being
part of an important socio-economic zone in North-Eastern
China, the Luanhe River Basin (LRB) has undergone rapidsocio-economic development in the last 40 years that has
been significantly influenced by Central and local government policies, leading to tremendous changes of land use,land cover and environment. It provides an ideal case study
to investigate and understand the change of the flood risk
influenced by different levels of policies and climate change
and its linkages with SDG realisation at a basin level, which
has not been done. Therefore, this study presents an integrated framework to systemically analyse flood risk under
different socio-economic development strategies and climatechange scenarios in LRB, providing essential information tobetter understand the interlinkages between different SDGs.
Based on a high-performance hydrodynamic flood modeland open data, the flood risk assessment framework can beapplied to river basins in other developing countries where
access to high-quality data is limited.

Study area and datasets
This work focuses on a river basin/catchment that provides
a natural hydrological unit to support flood risk assessment
and understand the impact of national and basin-level policies on local development and SDG achievement. Being
home to a large population and 27 counties in Hebei Province, Inner Mongolia and Liaoning Province, the case study,
LRB, is an important socio-economic zone on its own inNorth-Eastern China, and also directly contributes to andinfluences the socio-economic development of the BeijingTianjin-Hebei region.

Fig. 1  Location and terrain of
LRB and the locations of precipitation stations and reservoirs

13Sustainability Science

Luanhe River Basin (LRB)
The 44,750 ­km2 LRB is located in the northeast of theNorth China Plain (115°30ʹ E-119°45ʹE, 39°10ʹ N-42°40ʹ
N) (Fig. 1). The 888 km long Luanhe River originates from
Bayanguertu Mountain, Fengning County, Hebei province,
flows through Hebei Province, Inner Mongolia and Liaoning
Province, and finally enters Bohai Bay at Laoting County(Liu 2012). About 98.2% (43,940 ­km2) of the river basin
area is classified as mountainous landscapes with the dramatic change of elevation from north to south, before thebasin enters a low elevation coastal plain at its southern end.
The climate across the basin changes from semi-arid temperate continental monsoon to semi-humid, and finally tohumid from northwest to southeast (Bi et al. 2018). According to the records between 1982 and 2015, the basin-wide
mean annual temperature and precipitation are 7.0 ± 2.6 ℃
and 488.4 ± 80.7 mm, respectively (Wu et al. 2020). The
precipitation demonstrates a clear heterogeneous seasonal
distribution with the main wet period in July and August
every year (Sheng and Xiuling 2004; Lu 2005; Jiang et al.
2019). Overall, the basin has a complex climate pattern featured with four distinct seasons, significant monsoon effects,
concentrated precipitation and heat over the same period.
LRB has a population of approximately 7.23 million, andthe average density of population is 162 people/km2. The
GDP per capita is about 50% more than the national average(Liu 2012). The predominant land use types in the basin areforest, agriculture, grassland and urbanised areas (Li et al.
2018). In particular, LRB provides an essential ecologicalbarrier to alleviate the impact of sandstorms that originate in
Mongolia on Beijing and the surrounding areas. With threelarge reservoirs, at Panjiakou, Daheiting and Shuangfengsi(under construction) (Fig. 1), the basin provides importantwater resources for the Beijing-Tianjin-Hebei region, whichis the most socio-economically active region in North China(Wu et al. 2020), and thus the LRB directly contributes tothe region’s development.
As shown in Fig. 1, the LRB becomes significantly narrower in its furthest downstream reaches. This, combinedwith the upstream mountainous landscape, leads to rapidly
converging flood flows during wet seasons, increasing floodrisk in the downstream floodplain areas where major cities,
including Qinhuangdao and Tangshan, are located (Shaoet al. 2001). The basin’s specific climate and landscape features make it vulnerable to large flood events (Han 1999;
Yang and Yang 2013). Therefore, LRB provides an ideal
case study to investigate the linkages between rapid economic development, key infrastructure construction (damsand reservoirs), environmental protection (afforestation to
mitigate sandstorm impacts), flood risk and climate change.
Particularly, the rapid land use change as a result of accelerating economic development has created significant impacts

13

on hydrological processes, which, combined with climatechanges, may significantly change flood risk across thebasin. However, whilst it can provide essential information
to support policy-making and planning for LRB's sustainable future, basin-wide flood risk assessment under different
development strategies has not been conducted so far.

Data
The proposed flood risk assessment framework (see “Theflood risk assessment framework”) adopts a high-performance hydrodynamic model to predict flood inundation,
which requires spatial data, including a digital elevation
model (DEM) and land use maps covering the whole domain
for model set-up. Three sets of rainfall data are used to drive
flood simulations, including historical rainfall records fromthe surrounding meteorological stations for reproducing ahistorical flood event for model calibration, city-wise design
rainfall to drive the simulation of flood scenarios and different Representative Concentration Pathway (RCP) data to
account for the impact of future climate change. Climate
projections were extracted from the NASA Earth Exchange
Global Daily Downscaled Projections (NEX-GDDP) dataset, which provides downscaled climate scenarios across theglobe, derived from the simulation results from 21 General
Circulation Models (GCMs) through the Coupled Model
Intercomparison Project Phase 5 (CMIP5) for both RCP4.5
and RCP8.5 (Thrasher and Nemani 2015). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the period between
1950 and 2100, wherein precipitation from 1950 to 2005
is defined as the 'retrospective run' and from 2006 to 2099
as the 'prospective run'. Furthermore, open remote sensing
data are available to provide observed flood extent for validating model results. The Gridded Population of the WorldVersion 4 (GPWv4) is further used to assess the population
at risk. The key datasets used in this study are summarisedin Table 1. The current risk assessment framework mainly
uses open data to ensure the transferability of the approachto other river basins, especially those in developing countries
where data availability may be limited.

The flood risk assessment framework
In the flood risk assessment framework developed hereinfor LRB, city-wise design rainfall is first derived to drive
flood inundation modelling using the High-Performance
Integrated hydrodynamic Modelling System (HiPIMS)(Liang and Smith 2015; Xia et al. 2019). The flood modelling results are then combined with the land use and population data to evaluate flood impact and indicate flood risk.
The different components/procedures considered in theSustainability Science
Table 1  The datasets used by the flood risk assessment framework, and their sources
Data type

Scale/resolution

Data source

Spatial data
DEM
Land use data

90 m × 90 m
90 m × 90 m

Meteorologic data
Daily rainfall
Design rainfall
RCPs

MERIT Hydro: (http://​hydro.​iis.u-​tokyo.​ac.​jp/​~yamad​ai/​MERIT_​Hydro/)
Resources and Environmental Sciences Data Centre, Chinese Academy of Sciences (http://​www.​resdc.​
cn/)

10 stations
8 cities
0.25° × 0.25°

Remote sensing
Landsat 7

China Meteorological Data Service Centre (http://​data.​cma.​cn)
Hydrological Yearbook
NASA Earth Exchange Global Daily Downscaled Projections (https://​www.​nccs.​nasa.​gov/​servi​ces/​data-​
colle​ctions/​land-​based-​produ​cts/​nex-​gddp)

30 m × 30 m

MODIS

250 m × 250 m

Population
GPWv4

Landsat 7 top-of-atmosphere (TOA) reflectance image collections available from Google Earth Engine(http://​earth​engine.​google.​org), collected originally by USGS/EROS (http://​lands​at.​usgs.​gov/)
16-day MOD13Q1 version 6 Terra Vegetation Indices product available from Google Earth Engine(http://​earth​engine.​google.​org), collected originally by the NASA Earth Observing System (EOS)(ftp://​ladsw​eb.​nascom.​nasa.​gov/​allDa​ta/6/)

1000 m × 1000 m

Centre for International Earth Science Information Network—CIESIN—Columbia University. 2018.
Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades,
NY: NASA Socio-economic Data and Applications Center (SEDAC) ( https://​doi.​org/​10.​7927/​H49C6​
VHW)

flood impact/risk assessment are introduced in the following sections.

Design rainfall and climate change impact
The Chicago design storm model (Keifer and Chu 1957)
is used to obtain the intensity–duration–frequency (IDF)
curves for different administrative zones (i.e., cities) in LRB.
The average rainfall intensity of a certain frequency (return
period) can be defined as:i=A(1 + C lg P)(D + b)u

Table 2  Parameters for deriving city-wise design storms in LRB
City

A1

C

b

u

TangshanZhangjiakou
ChengdeChaoyang
Qinhuangdao
Huludao
Chifeng
Xilinguole

11.90
22.66
17.74724.50
3.6335
13.20
9.60
6.76

0.69
0.906
0.789
0.979
0.711
0.85
1.35
1.55

10.23
15.479
14.72
18.92
1.04
7
10
12.77

0.702
0.948
0.829
0.92
0.464
0.8
0.8
0.715

(1)

where i is the average rainfall intensity (mm/min), D is thestorm duration (minutes); P is the return period (years;
only 100-year events are considered in this work and so
P = 100 years); A, C, b and u are model parameters representing local hydrological conditions, which are usuallyassumed to be constant and estimated by fitting the historical
data (Keifer and Chu 1957). Herein, D = 24 × 60 min, i.e.
1-day event, is considered to maintain consistency with thedaily climate data and a similar approach has been adopted
by other researchers (e.g. Xu et al. 2012; Gilroy and McCuen
2012; Schuster et al. 2012; Chang et al. 2013).
From Eq. (1), a storm advancement coefficient r = 0.4 (i.e.
the ratio between the time to peak and rainfall duration) is
further defined, based on which the rainfall distribution/hyetograph can be calculated as follows:[]A(1 + C lg P) (1 − u)t∕Rt + bi(t) =()u+1t∕Rt + b(2)

with Rt = r for t < Dp, and Rt = 1 − r for t ≥ Dp, where Dp
is the time to peak. For different cities in the LRB, the relevant parameters of the above Chicago design storm modelare obtained from the Chinese Hydrological Yearbook andsummarised in Table 2.
To consider the potential climate change impact on
rainfall and thus flood risk, the climate change scenarios
RCP4.5 and RCP8.5 (IPCC 2014) are used. The uplift factors derived from the climate change modelling results areadded to the city-wise IDF curves to account for the change
to rainfall induced by climate change in 2030. To obtainthe uplift factors, a Log-Pearson type III distribution (Chow
1988) was fitted to the NEX-GDDP dataset to calculate the13Sustainability Sciencemagnitudes of 100-year design rainfall at each pixel within
the domain for the ‘retrospective run’ (i.e. the historicalperiod between 1950 and 2005) without climate change to
give Rhis (mm/day), and future periods (2006–2030) withclimate change (RCP4.5 and RCP8.5) to provide Rcli(mm/day). The pixel-based climate change uplift can then be
calculated as up2006,2030 = Rcli ∕Rhis . However, the baseline
year for the land use analysis is 2015, so the uplift values
are adjusted accordingly to be consistent with the land usechange scenarios. Assuming the impact of climate change on
the design rainfall distributes linearly against time, the uplift
factors calculated from 2015 are obtained
via linear() interpolation, up2015,2030 = 8up2006,2030 ∕ 3up2006,2030 + 5 , and thefinal results are presented in Fig. 2. The pixel-based uplift
factors ( up2015,2030 ) are then resampled to 90 m resolution
to be consistent with the DEM and land use data, and arefurther averaged to create the city-wise uplifts (right panelof Fig. 2, with the city boundaries illustrated in the central
panel). These uplift factors are then applied to the design
rainfall to account for climate change impact. The design
rainfall (uniformly distributed across each of the pixels),with and without considering climate uplifts, will be used
to drive flood modelling.

Future land use and flood scenarios
The design rainfall is combined with the future land use distributions to create flood scenarios to support further flood
impact/risk analysis. Four future land use scenarios, labelled
as “Trend”, “Expansion”, “Sustainability” and “Conservation”, were designed and projected to 2030 by Xu et al.
(2021) using the CLUMondo model (Asselen and Verburg
2013) to reflect different socio-economic development andenvironmental protection strategies, local development plansand policies (Fig. 3 and Table 3). The “Trend” scenario isprojected as a pathway to maintain “business as usual”, i.e.,
to follow the current development trend. The “Expansion”
scenario follows the fossil-fuelled development or sharedsocio-economic pathway (SSP5) (O'Neill et al. 2014), in
which abundant fossil fuel resources are exploited, theglobal economy grows at the highest speed, and the global
urban population reaches 92% in 2100. The “Sustainability”
scenario is based on the sustainable shared socio-economic
pathway (SSP1) (O'Neill et al. 2014). Finally, the “Conservation” scenario adopts the socio-economic context of the“Sustainability” scenario as a baseline, but it is extended
by further implementing ecological restoration and environmental protection policies in the LRB.
The original 100-year design rainfall (without considering climate change impact) and the two sets of uplifted

Fig. 2  City-wise climate uplift: pixel-based uplift data, with the pixel uplift value range from 0.9 to 2.0 (left panel); city mask specifying the cityboundaries for uplift estimation (centre panel); and city-wise uplift factors (right panel)

13

Sustainability ScienceFig. 3  The land use distributions of LRB at the current stage and under different socio-economic development strategies
Table 3  The areas (­ km2)
of different land use types:
comparing the four future land
use scenarios with the 'Baseline'
condition in 2015

Land use type

CroplandSocio-economic scenarios
Baseline
10,258Trend
10,351
Expansion
10,352
Sustainability
10,360Conservation
10,314design rainfall (considering RCP4.5 and RCP8.5 climate
scenarios) are combined with the four projected land use
patterns to create 12 flood scenarios. Taking two large reservoirs, Panjiakou and Shuangfengsi Reservoirs, as examples,
further flood scenarios are designed to investigate the impactof this key type of infrastructure on flood risk. Since theShuangfengsi Reservoir is currently under construction, it
is not included in the original 12 flood scenarios. Considering, respectively, removing the Panjiakou Reservoir fromand adding the Shuangfengsi Reservoir to the simulations,another 16 flood scenarios are created. Together with the2015 Baseline simulation, it gives a total of 29 flood scenarios, as summarised in Table 4, which are simulated using
HiPIMS in this work.

ForestGrassland withlivestock

Water

Built-up

Unused land

16,926
13,957
14,723
16,112
17,631

14,101
15,62916,64915,327
14,091

718
1003
965
1093
1033

1633
3803
1977
1684
1678

1114
7.1
84
175
1.9

Flood modelling
To predict the rainfall-induced flooding process and theresulting inundation, the hydrodynamic flood model HiPIMS(Xia et al. 2019) is used in this work, which solves the shallow water equations (SWEs) written in matrix form as

𝜕q 𝜕f 𝜕g++=S
𝜕t 𝜕x 𝜕y(3)

where t is time; x and y are the two spatial Cartesian coordinates; q is the vector of conserved flow variables; f and g are
the vectors of fluxes in the x- and y-directions, respectively;
S contains different source/sink terms. The vector terms are
given by:13

Sustainability Science
Table 4  The 29 flood scenarios designed to support flood risk analysis
Rainfall100-year design rainfall

Land use
Land use (2015)
Trend (2030)

Baseline
Trend

Expansion (2030)

Expansion

Sustainability (2030)

Sustainability

Conservation (2030)

Conservation

100-year design rainfall (RCP4.5) 100year design rainfall (RCP4.5)

100-year design rainfall (RCP8.5)

Trend (RCP4.5)
Trend (RCP4.5) − P
Trend (RCP4.5) + S
Expansion (RCP4.5)
Expansion (RCP4.5) − P
Expansion (RCP4.5) + S
Sustainability (RCP4.5)
Sustainability (RCP4.5) − P
Sustainability (RCP4.5) + S
Conservation (RCP4.5)
Conservation (RCP4.5) − P
Conservation (RCP4.5) + S

Trend (RCP8.5)
Trend (RCP8.5) − P
Trend (RCP8.5) + S
Expansion (RCP8.5)
Expansion (RCP8.5) − P
Expansion (RCP8.5) + S
Sustainability (RCP8.5)
Sustainability (RCP8.5) − P
Sustainability (RCP8.5) + S
Conservation (RCP8.5)
Conservation (RCP8.5) − P
Conservation (RCP8.5) + S

Herein, ‘P’ represents Panjiakou Reservoir (including Daheiting); ‘S’ denotes Shuangfengsi Reservoir; ‘ + ’ and ‘–’means adding and removing
the respective reservoirs

qy
qx
⎡
⎤
⎡h⎤
⎡
⎤
⎥,
qx qy ∕hq = ⎢ qx ⎥, f = ⎢ q2x ∕h + gh2 ∕2 ⎥, g = ⎢
⎢ 2
⎥
⎢ ⎥
⎢
⎥
2
q
∕h
+
gh
∕2
q
q
q
∕h
⎣ y
⎦
⎣ y⎦
⎣
⎦
x y

0
⎤
�
0
⎤ ⎡
⎡R−I⎤ ⎡⎢⎥
2
2
2
S = R + Sb + Sf = ⎢ 0 ⎥ + ⎢ −gh𝜕z∕𝜕x ⎥ + ⎢ −Cf qx qx + qy ∕h ⎥
�
⎢
⎥ ⎢
⎥ ⎢
⎣ 0 ⎦ ⎣ −gh𝜕z∕𝜕y ⎦ ⎣ −Cf qy q2 + q2 ∕h2 ⎥⎦xyin which h is the water depth; qx = uh and qy = vh are theunit-width discharges in the x- and y-directions, with u andv being the corresponding depth-averaged velocity components; g = 9.81 m/s2 is the acceleration due to gravity. The
source terms S are subdivided into the mass source terms R
including rainfall rate (R) and infiltration rate (I), the slope
source terms Sb and the friction source terms Sf to accountfor the effects of gravity and friction, respectively. Wherein,z is the bed elevation extracted from the DEM data; Cf = g
n2/h1/3 is the friction parameter with n being the Manningcoefficient that can be assigned according to different land
use types. The infiltration rate I is estimated using theGreen–Ampt method (Heber Green and Ampt 1911), which
may be written as][
𝜃e − 𝜃iedf+1
I = − = −Ks (𝜓 + h)(5)dtf
where Ks is the hydraulic conductivity (m/s); 𝜓 is the capillary head (m); 𝜃e = 𝜃s − 𝜃r and 𝜃ie = 𝜃i − 𝜃r are respectively the effective porosity and initial effective soil moisture, with 𝜃s, 𝜃i and 𝜃r defined as the saturated water content,
initial water content and residual water content; f is the

13

(4)

cumulative infiltrated depth. The parameters Ks , 𝜓 , 𝜃e and𝜃ie are assumed to be constants throughout a simulation.
Equation (5) may be solved using a backward Euler method
to estimate the infiltration rate, as introduced in Xia et al.
(2019).
In HiPIMS, the above governing SWEs are solved usinga Godunov-type finite volume method implemented with thelatest flux and source term discretisation schemes introducedby Zhao and Liang (2021) for stable and accurate prediction of overland flows and flooding processes. HiPIMS is
implemented on multiple GPUs to achieve high-performancecomputing and has been widely applied and tested for thesimulation of different types of flooding processes (e.g., Xia
et al. 2019; Xing et al. 2019). During the simulations, theresolution of the computational grid is taken to be the sameas the DEM data, and the time step is controlled by the CFL
condition. In this work, HiPIMS will be calibrated for application in LRB by reproducing a historical flood event.

Sustainability ScienceFig. 4  Rainfall records during
the 2012 Typhoon Saola andDamrey from Meteorological
Monitoring Stations

Flood impact/risk assessment
Due to the lack of data to define flood vulnerability andderive flood damage functions/curves, we combine floodmodelling results with land use data to quantify the accumulative inundation areas against water depth for different
land use types to indicate flood risk. The information can befurther integrated with damage functions/curves to estimate
flood loss when such data become available. The GPWv4
population distribution data (CIESIN 2018) are overlaid
with the inundation maps to estimate the number of people
affected by a flood and specify the potential hazard level.
For large-scale flood impact analysis, the definition reported
by Kang et al. (2006) is adopted, which classifies the hazard level to people according to flood depth as “low risk”(0.3–1 m), “moderate risk” (1–1.5 m), “high risk” (1.5–2 m)and “extremely high risk” (> 2 m).

Calibration of HiPIMS for application in LRB
To calibrate HiPIMS for flood modelling in LRB, we consider the flood event caused by Typhoon Saola and Damrey
in 2012. The Typhoon brought heavy rainfall (Fig. 4) to LRB
from late July to the beginning of August 2012, leading tosevere flooding in the downstream areas. The event caused
32 deaths and 12.3 billion RMB of economic losses in Hebei
Province, which was one of the most severe flood disasters
in LRB (Yang and Yang 2013).
The 90 m MERIT DEM is used to discretise the computational domain covering the whole LRB, resulting in 7.08
million valid cells for numerical calculations. Since thewhole river basin is considered, the computational domain is
only hydrologically linked to the outside through a tidal rivermouth. The Luanhe River enters the Bohai Sea through ariver mouth exposed to weak tides. The average tidal heightht ranges between 1–1.5 m (Gao and Shanming 1981) andthe average slope of the river mouth area is about S = 1/2378,
which may be used to estimate the backwater extent as
0.7ht/S = 1664–2497 m. This suggests that the tidal boundary does not pose any significant influence on the flooding
processes in the basin. Therefore, the measured rainfall asshown in Fig. 4 is used to drive the flood simulation and afree outflow boundary is imposed in the whole domain. The
simulation lasts for 16 days, starting at 0:00 on 21st July andending at 0:00 on 6th August 2012.
To obtain the necessary data to validate the flood simulation results, the flood footprints of the region in 2012 are
extracted from satellite data, including Landsat 7 (US Geological Survey 2019) and the Moderate Resolution Imaging
Spectroradiometer (MODIS) (Didan et al. 2015). Nine Landsat 7 images acquired during the flooding period are processed using a data-driven Random Forest model to extract
all of the water surfaces, including rivers, reservoirs/lakes
and ponding areas (refer to Chen et al. (2020) for detailed
implementation). However, certain inherent limitations of
the Landsat 7 data may lead to underestimation of the actual
water surface extent and thus flood footprints (Chen et al.
2020). Therefore, the vegetation response captured by the
MODIS images is also used to identify the flood footprints
by detecting the changes to the Enhanced Vegetation Index(EVI) (refer to Chen et al. (2019) for detailed implementation). The final footprints of the 2012 flood are then produced by combining the Landsat 7 water surface extent anddetected vegetation response area.
One of the key model parameters in HiPIMS is the Manning n, which may be generally set according to land use
types using textbook values (Chow 1988). However, for thebuilt-up area, due to the use of a relatively coarse computational grid (90 m), it is necessary to calibrate the Manning n
for this land use type to account for the blockage effects created by the dense buildings and other engineering structures13

Sustainability Science
Table 5  Model parameters forthe application of HiPIMS in
LRBParameters

Land use
Cropland
ForestGrassland with livestock
Water
Built-up
Unused land

Manning coeffi- Hydraulic conductiv- Capillary head 𝜓 Effective soil
cient n ­(sm−1/3) ity K (1.0 × ­10–7 m/s) (1.0 × ­10–2 m)water content
𝜃e (–)
0.035
0.1
0.035
0.04
0.15
0.03

that cannot be resolved by such a coarse-resolution grid.
The range of Manning n values for calibration is set to be[0.1, 0.15, 0.2]. Another set of model parameters is related
to infiltration, which is determined by the underlying soil
property but highly influenced by different land use types
in real-world applications (Thompson et al. 2010; Liu et al.
2012; Wang et al. 2013; Rogger et al. 2017; de Almeida et al.
2018). The main soil types in LRB are loam, sandy loam,
clay loam and sandy clay loam, and their infiltration properties are similar (Nachtergaele et al., 2009). The infiltration
parameters for the LRB flood simulation are initially set forthese soil types using textbook values as found in Chow
(1988) and further calibrated for different land use types.
Notably, LRB is typically a dry catchment throughout mostof the time in a year and no significant rainfall was recorded
before the flood event in 2012. Therefore, the initial soil
water content for the simulation is assumed to be equal to theresidual water content (i.e., 𝜃ie = 0.0), and the effective soil
water content 𝜃e is considered for model calibration. Afterneglecting infiltration for the “water” and “built-up” land
use types, a Sobol sequence method (Sobol′ 2001) is used
to create the calibration samples of infiltration parameters
for the remaining land use types including cropland, forest,
grassland with livestock and unused land. Combined withthe calibration samples of Manning n for the built-up area, atotal of 7800 groups of parameters are created and used formodel calibration. The final values of the model parameters
used in the simulation are summarised in Table 5.
To quantitively compare the predicted and observed flood
extents, the remotely sensed flood extent is resampled to
90 m resolution, i.e. the grid resolution adopted in the simulation. The overlap ratios are then calculated as follows:

O𝐏𝐑 = CNZ(𝐏𝐑 ⊙ 𝐑𝐒)∕CNZ(𝐏𝐑) and O𝐑𝐒= CNZ(𝐏𝐑 ⊙ 𝐑𝐒)∕CNZ(𝐑𝐒)(6)where O ( 0 ≤ O ≤ 1) is the overlap ratio with O = 0
indicating no overlap and O = 1 representing a perfectmatch; PR is a Boolean matrix of the numerical results anda cell with water depth larger than 0.3 m is defined to be

13

9.44
1.93
2.08
0.0
0.0
2.1

9.07
8.0
6.89
0.0
0.0
4.0

0.434
0.309
0.401
0.0
0.0
0.423

inundated; RS is a Boolean matrix of the remote sensing
data with 0 for dry and 1 for inundated pixels; 𝐏𝐑 ⊙ 𝐑𝐒 is
the Hadamard product to calculate the overlap matrix; CNZ
counts the non-zero values in the corresponding matrices.
The flood extent reproduced by HiPIMS for the 2012
event with the calibrated parameters is presented in Fig. 5,comparing with the observed extent extracted from the satellite data. The overlap ratios are respectively O𝐏𝐑 = 0.75 and
O𝐑𝐒 = 0.71, i.e., more than 70% of the observed inundated
area is predicted to be flooded by HiPIMS. The modelling
results are considered to be satisfactory for such a large-scaleand relatively long simulation and the modelling approachis therefore deemed to be sufficient for the following flood
simulations to support risk assessment in the LRB.

Results
The 29 flood scenarios introduced previously are simulated
to produce results to investigate and discuss the flood risk
in LRB under different development strategies and climatechange. Driven by 100-year design rainfall, the baseline
simulation for comparison is based on the 2015 land use,
and the resulting basin-wise inundation map is presented
in Fig. 6, showing significant inundation of the floodplains
along the rivers.

The impact of land use change on flooding
Figure 7 shows the simulation results of the 100-year
floods in terms of accumulative inundated areas against
flood depth for each of the considered land use types forthe baseline and the four projected land use scenarios. The
cumulative curves represent the increment of the flooded
area (vertical axis) against the increase of the flood depth(horizontal axis). The total flooded area for each of theland use types is returned and the curve becomes horizontal after the maximum water depth is reached, i.e. after
all of the flooded area has been taken into account. The
total flooded areas for the baseline, “Trend”, “Expansion”,

Sustainability ScienceFig. 5  Comparison between the predicted and observed flood extents in the 2012 flood event: the zoomed-in areas located at downstream of Panjiakou reservoir (left); Qianan city (upper right); and Lulong county (lower right)
Fig. 6  The inundation map fromthe baseline simulation: inundation is marked in blue

“Sustainability” and “Conservation” simulations are calculated to be 7110, 7205 (+ 1.3%), 7173 (+ 0.9%), 7174(+ 0.9%) and 7184 (+ 1.0%) ­k m 2, respectively, wherethe values in the brackets indicate the percentage of theincrement compared to baseline result. Clearly, the total
flooded areas predicted for the different scenarios do not13Sustainability ScienceFig. 7  Accumulative inundated areas against water depth under the baseline and the other four scenarios without considering climate change
impactschange much. This is because the inundation area is controlled mainly by the topography of the domain rather thanthe land use for an extreme rainfall event as consideredherein. However, the distribution of the inundated land
use types changes significantly across different scenarios.
In the baseline scenario, whilst occupying a smaller area
than forest and grassland with livestock (refer to Table 3),
cropland is the most inundated type of land use. The reason is that croplands are mainly located in floodplains andrelatively low-lying areas. The final inundated areas forwater bodies, built-up areas, and unused land are similar.
Under the “Trend” land use/development scenario,
rapid urbanisation causes expansion of the urban area
(Table 3). This leads to a significant increase in the inundation of the built-up area. Similarly, the increased inundation of grassland with livestock is due to the expansionof this land use type. Although its total area increases,
the inundated area of cropland decreases. This reduction
may reflect the relocation of croplands to higher grounds
because of rapid urbanisation at the lower levels. Whilst
the forest area decreases, the inundated area remains
largely unchanged compared to the baseline. The reduction of the unused area leads to the inundation of this typeof land use becomes almost zero.

13

The “Expansion” scenario follows the global pattern of development, but since China has been the fastest developing country globally in the last 40 years, therate of development under the “Trend” scenario is moreaggressive than “Expansion”. Therefore, compared withthe flood simulation results for the “Trend” scenario,
the built-up area in “Expansion” is smaller, and so is thecorresponding inundated area. The inundated areas of allother land use types show little change, except that theinundated area of the grassland now becomes slightly
larger than that of cropland. The “Expansion” scenario
predicts slightly more “unused land” which is reflectedin the inundation result.
For “Sustainability” and “Conservation”, it is noticeable that the predicted inundation of the built-up areas
decreases in comparison with the baseline simulation,although the total built-up areas slightly increase for bothof these development/land use scenarios. While the total
areas of forest in both scenarios do not show a significant change from the baseline, the inundated areas bothincrease significantly, reflecting the redistribution of land
use types to support sustainable development.

Sustainability ScienceFig. 8  Accumulative inundated areas against water depth for thefour future flood scenarios, after considering climate change impacts(through RCP4.5 and RCP8.5 climate scenarios). The top two panels
show the inundated areas predicted for each of the six key land use

types under the RCP4.5 and RCP8.5 climate scenarios, respectively.
The bottom panel shows the corresponding ratios between the inundated areas with and without considering climate change

Fig. 9  The inundated areas ofthe six land use types predictedby different flood simulations

13

Sustainability Science
Table 6  Affected population(AP) (per 100,000 people)
predicted on the four future
land use scenarios with andwithout considering climatechange, in comparison with thebaseline simulation, and thecorresponding RCs calculatedusing Eq. (7)Affected level

Land use scenario
Baseline
Trend
Expansion
Sustainability
Conservation
Trend (RCP4.5)
Expansion (RCP4.5)
Sustainability (RCP4.5)
Conservation (RCP4.5)
Trend (RCP8.5)
Expansion (RCP8.5)
Sustainability (RCP8.5)
Conservation (RCP8.5)

Low risk

High risk

Extremely high
risk

AP

RC

AP

RC

AP

RC

AP

RC

6505
6580
6605
6605
6602
6770
6787
6788
6781
7109
7127
7131
7121

1.15
1.53
1.53
1.50
4.07
4.33
4.35
4.24
9.28
9.56
9.63
9.47

1943
1992
1982
1978
1994
2028
2024
2024
2044
2154
2154
2149
2169

2.54
1.98
1.80
2.63
4.38
4.15
4.19
5.19
10.85
10.88
10.62
11.61

1283
1304
1285
1294
1304
1370
1344
1342
1351
1452
1436
1440
1453

1.67
0.19
0.83
1.65
6.76
4.77
4.56
5.30
13.20
11.93
12.22
13.21

4290
4367
4323
4309
4304
4678
4642
4634
4630
5603
5557
5544
5538

1.79
0.77
0.44
0.32
9.05
8.21
8.02
7.93
30.61
29.53
29.23
29.08

Impact of climate change on flooding
Driven by the uplifted 100-year design rainfall, eight further flood simulations (Table 4) are run to consider climatechange impact based on both RCP4.5 and RCP8.5 climate
scenarios, and the simulation results are illustrated in Fig. 8.
Whilst the distribution of inundation across different typesof land use remains largely unchanged, both RCP4.5 and
RCP8.5 climate scenarios cause an increase in the inundated
areas across all land use types, with the RCP8.5 scenario
having a greater impact. These results reflect that both ofthe climate scenarios predict an increase in precipitation
across the basin. These simulation results demonstrate thatclimate change may lead to more areas being flooded andsubsequently an increase in the flood risk in LRB in 2030.
Figure 9 compares the inundated areas predicted for thesix key land use types in the 13 flood simulations (the baseline simulation and the 3 simulations (i.e. with and without
considering the two climate change scenarios) on each of thefour future land use projections). The baseline simulation
predicts the maximum inundation of unused land because
this land use type is projected to decrease dramatically toalmost zero in all four development scenarios. However, it
should be noted that the baseline simulation also returns the
maximum inundated area for cropland, although the areaof cropland increases in all four land use change scenarios.
This apparent contradiction reflects the spatial redistributionof cropland across the basin to higher areas to make spacefor urban and industrial activities as the land use changes.
The simulations under the RCP8.5 climate scenario predictthe maximum inundated areas for all other land use types

13

Moderate risk

as RCP8.5 projects increased rainfall across the basin in the
future.

Flood impact on population
The inundated areas are mainly distributed along rivers andadjacent floodplains where most of the population and socioeconomic activities are located. The exposure of the population to flooding can be spatially identified by overlaying thepredicted inundation maps with the population distribution
dataset GPWv4. Since there is no projected population distribution data in 2030, the 2015 GPWv4 dataset is used in
all of the following population exposure analyses. Assuming
population distribution will remain unchanged (thus representing a conservative estimate), we explore how land use
and climate changes influence the exposure of the population
to flooding. The flood hazard level to people can be classified based on the predicted flood depths (“Flood impact/risk
assessment”). To quantitatively compare the results with thebaseline simulation, the relative change to the at-risk population is calculated as:()
RC = Pops − PopB ∕PopB × 100%
(7)
­ op B are respectively the affected
where ­Pop s and P
population estimated for the future flood scenarios andthe baseline simulation. The results are summarised inTable 6, showing the affected population (AP) per 100,000
people at different hazard levels for all of the 12 futureSustainability ScienceFig. 10  Flood extents (zoom-in views to show the flooded areas adjacent to the reservoirs) predicted for the “Conservation” and “Trend”
land use scenarios considering infrastructure construction: a without

Shuangfengsi Reservoir; b with Shuangfengsi Reservoir; c with Panjiakou and Daheiting Reservoirs; d without Panjiakou and Daheiting
Reservoirs

scenarios and the relative change in comparison with thebaseline simulation.
The results show that, across all of the scenarios, large
proportion of the population is exposed to low (7,000
per 100,000 papulation on average) and extremely high
(5,000 per 100,000) flood risk, with smaller proportion ofpeople having moderate or high risk (c. 2,000 and 1,000
per 100,000 population, respectively). Without climatechange, the number of people exposed to flooding does
not undergo substantial change under all the four future
land use scenarios, with the largest RC calculated to besmaller than 2.7%. Including climate change shows substantially increased flood risk to people. Under the worst
RCP8.5 climate scenario, the number of people exposed
to “extremely high risk” increases by c. 30% compared tothe baseline.
The distribution of affected population at different
hazard levels across the four land use scenarios does notshow clear patterns, but the “Trend” development strategy
will lead to the maximum number of people exposed to
extremely high risk no matter whether climate change isconsidered or not. Conversely, the “Conservation” development is predicted to be the most effective at reducing
the number of people subject to extremely high risk fromflooding. The positive RC values show that all four development strategies will increase flood risk to people, especially under climate change conditions. This may be dueto the increase of built-up areas in the low-lying areas thatare more vulnerable to flooding.

Potential impact of the construction of key
infrastructure on flooding
LRB provides important water resources for the downstream
cities and the Beijing-Tianjin-Hebei region, and several large
reservoirs have been constructed (Panjiakou, Daheiting) orare under construction (Shuangfengsi). Large reservoirs may
significantly influence downstream flood risk. Therefore,
further simulations (Table 3) are conducted to investigate
the impact of Panjiakou and Shuangfengsi reservoirs on
flood risk.
Taking the “Conservation” and “Trend” land use scenarios as examples, Fig. 10 presents the zoomed-in maps
to show the impact of the reservoirs on the local inundation extents. The results shown in Fig. 10a and b indicatethat including Shuangfengsi Reservoir in the simulation
evidently reduces the predicted flood extent downstream,
potentially providing an effective measure to mitigate
the downstream flood risk. Similarly, the Pandjiakou andDaheiting Reservoirs also impact the downstream flood risk,
and the simulations without including the reservoirs predict
more severe flooding and greater flood extents downstream(Fig. 10c and d). However, due to the large size of the basin,
the impact of these reservoirs on overall inundation extent
across the basin is limited for all of the simulated scenarios(the total flooded areas when removing/including Panjiakou/
Shuangfengsi reservoirs under “Trend” development strategy with RCP8.5 climate scenario are calculated to be 8172
and 8163 ­km2, respectively).

13

Sustainability Science
Table 7  Affected population (AP) (per 100,000 people) predicted on the future land use and climate scenarios also considering the impact of key
infrastructure construction
Affected level

Land use scenario
Trend (– P, RCP4.5)
Expansion (– P, RCP4.5)
Sustainability (– P, RCP4.5)
Conservation (– P, RCP4.5)
Trend (– P, RCP8.5)
Expansion (– P, RCP8.5)
Sustainability (– P, RCP8.5)
Conservation (– P, RCP8.5)
Trend (+ S, RCP4.5)
Expansion (+ S, RCP4.5)
Sustainability (+ S, RCP4.5)
Conservation (+ S, RCP4.5)
Trend (+ S, RCP8.5)
Expansion (+ S, RCP8.5)
Sustainability (+ S, RCP8.5)
Conservation (+ S, RCP8.5)

Low risk

Moderate risk

Extremely high risk

AP

RCI

AP

RCI

AP

RCI

AP

RCI

6715
6734
6733
6725
7039
7062
7064
7050
6833
6848
6851
6845
7189
7203
7209
7197

− 0.81− 0.79− 0.81− 0.83− 0.98− 0.91− 0.94− 0.99
0.93
0.90
0.93
0.95
1.13
1.07
1.09
1.07

2030
2021
2025
2048
2121
2125
2120
2146
2039
2039
2036
2053
2197
2195
2190
2203

0.10
− 0.140.05
0.18− 1.54− 1.38− 1.38− 1.02
0.52
0.77
0.56
0.43
2.00
1.86
1.89
1.58

1354
1327
1321
1333
1466
1449
1455
1464
1358
1335
1336
1345
1454
1439
1440
1458

− 1.11− 1.29− 1.53− 1.35
0.92
0.88
1.04
0.79
− 0.86− 0.72− 0.44− 0.43
0.11
0.19
0.04
0.39

5213
5170
5159
5148
6202
6150
6134
6118
4161
4132
4127
4128
4985
4944
4934
4937

11.42
11.36
11.31
11.18
10.69
10.67
10.65
10.48
− 11.06− 10.99− 10.94− 10.85− 11.03− 11.03− 11.01− 10.84

However, downstream of these reservoirs are urbanised
areas with high population densities, and so the impact of
the reservoirs on the safety of the downstream population
may affect the basin-wise statistics. To assess this, the relative change to the at-risk population related to infrastructure
construction (RCI) is calculated against the previous flood
simulation results as:()
RCI = Pops,I − Pops ∕Pops × 100%(8)where ­Pops,I is the population affected by flooding after
including/removing reservoirs, as appropriate. The numbers of affected population from different simulations andthe values of RCI are summarised in Table 7. The results
clearly indicate that these large reservoirs reduce the risk topeople downstream, and the values of RCI are calculated tobe ± 10% for the extremely high-risk population in all of thesimulated scenarios.

Discussion and conclusion
Driven by the city-wise 100-year design rainfall, a total of
28 future flood scenarios are simulated in this work using ahigh-performance flood inundation model to better understand the basin-scale flood risk in the whole LRB. The
scenarios vary by different land use, climate change andkey infrastructure construction conditions. The four future
land use scenarios are projected to 2030 to reflect different

13High risk

development strategies and used to explore the relationships
between flood risk, policy-driven development and realisation of Sustainable Development Goals.

Flood risk under different development and climate
scenarios
The flood simulation results show that different land usechanges do not significantly influence the total inundated
area across the basin for the extreme 100-year rainfall underconsideration. This is because flood inundation is distributed
mainly according to hydrological processes and the basin
topography rather than land use under the extreme rainfall
conditions. However, the inundated areas of individual land
use types vary significantly in different scenarios due to relocation and expansion/reduction of different types of land use.
For example, whilst the area of forested land is projected
to decrease in the “Trend”, “Expansion” and “Sustainability” land use scenarios, the inundation of this land use typeis predicted to increase in all four scenarios, potentially
due to the redistribution of forest areas into flood-prone
areas. Commonly being located on flat lands close to water
resources (rivers) and occupying a large area, the agricultural land use types including cropland and grassland withlivestock are most exposed to flooding and have the largest
inundated areas in all of the simulations, including the baseline simulation. The “Trend” scenario predicts rapid urban
expansion, including development in flood-prone zones.
This gives rise to a significant increase in the inundation of

Sustainability Sciencebuilt-up land, potentially putting a much larger population atrisk of flooding and creating much higher economic losses.
Climate change impacts are taken into account by deriving city-wise uplift factors to adjust the design rainfall
under the two IPCC recommended future climate scenarios,
RCP4.5 and RCP8.5. Climate change will increase future
rainfall intensity in LRB, leading to higher flood peaks andthe prediction of increased total inundated areas acrossthe basin. However, the distribution of the inundated areas
between different land use types does not show significant
change. The RCP8.5 climate scenario predicts a greater
uplift in rainfall than RCP4.5 and subsequently much greater
inundated areas across the basin and for most of the land use
types. These predictions are consistent for all four land use
scenarios (see Fig. 9).
Based on the 2015 global population distribution datafrom GPWv4, the number of people affected by flooding todifferent hazard levels is estimated. The population at riskof flooding does not show much change between the simulations under different land use scenarios. This insensitivity to
the development scenarios may be related to several potential reasons. A large portion of the population is located in
the low-lying areas close to rivers, which are always more
vulnerable to flooding. Whilst the built-up area may expand
to different extents in different development scenarios, thenewly developed areas will increasingly be located at higher
grounds and hence are less exposed to flooding. The existing urbanised zones will continue to develop but normallywill not be relocated and replaced by another land use type.
The population distribution is based on the 2015 data andhas not been projected to take account of potential changes
in total population by 2030, which represents a limitationof this work.
LRB contains several large reservoirs, and their impact on
downstream flood risk is also explored. Through simulations
with and without including reservoirs, it is found that, whilst
the reservoirs can mitigate flood risk in the downstream
areas close to their locations, their impact on the basin-wide
flood inundation is limited. However, due to the existenceof major cities downstream, the large reservoirs can effectively reduce the number of people at extremely high risk offlooding, and the relative change of the extremely high-risk
population can reach 10% for all of the simulated scenarios.

Interactions between Sustainable Development
Goals
All of the four future development strategies considered
for the LRB are projected to induce significant land usechange, which will influence future flood risk and affect
SDG achievement in LRB. As Northern China's most forested river basin, development strategies under the “Sustainability” and “Conservation” scenarios will further increase

the forest area. This directly links to SDG 15 on terrestrial
ecosystem conservation and management, including forests. The increase in cropland and grassland areas due tothe increase in livestock breeding will exert different impacts
on achieving SDG 2, which aims at ending hunger, achieving
food security, improving nutrition and promoting sustainableagriculture. Increased waters, e.g., through the constructionof reservoirs, water transport systems and irrigation systems,
will help achieve SDG 6, including its targets to address
water accessibility, improve water quality and water-use
efficiency, and protect water-related ecosystems, including
mountain forests and wetlands, rivers, aquifers and lakes/
reservoirs. Achieving SDG 6 can further generate synergies
with SDG 15 and SDG 2 via enhanced ecosystem services,
particularly related to the provision and regulation services
and improved irrigation systems. Moreover, as a vital water
source for Tianjin Municipality, the realisation of SDG 6
in LRB will contribute to achieving SDGs in Tianjin as
water is an essential resource supporting all aspects of lifeand social-economic development. The expansion of builtup land under the “Trend” and “Expansion” development
strategies will substantially increase the areas of human settlement and cities, contributing to economic growth throughinfrastructure development and industrialisation. This will
directly contribute to SDG 11 on sustainable cities and communities such as Target 11.1 on safe and affordable housing.
However, rapid urbanisation may lead to the generation ofpollutants, effluents and solid wastes, which could damage
public health (SDG 3), water resources (SDG 6) and related
ecosystems (Goal 15). Detailed analysis of the synergies andtrade-offs between SDGs under different development strategies in LRB can be found in Zhou et al. (2021).
Effective management of flood risk, or disaster risk reduction (DRR) in general, is directly or indirectly embedded in
the SDG framework and is a critical aspect for consideration when evaluating the impacts of national and regional
development strategies on SDGs. Based on the preceding
results and analysis, whilst different development strategies
may not lead to substantial changes in the total flooded area
across the basin, the inundated areas of different land use
types can be significantly changed and shifted. It is therefore
necessary to analyse in more detail the impact of flooding
on different land use types to better understand the complex
relationships between flood risk and socio-economic development and how the synergies and trade-offs between the
SGDs are affected. For example, the “Trend” development
strategy is predicted to see rapid socio-economic development and substantial expansion of built-up areas. This may
positively contribute to SDG 8 in terms of promoting economic growth and providing full and productive employment and decent work for all, and is therefore related to SDG
1 (No Poverty). However, this will inevitably increase floodinundation of built-up areas and pose higher flood risk to13

Sustainability Science

people, properties and businesses, which will in turn create
trade-offs for achieving other SDGs.
Climate change may increase the frequency and intensityof extreme rainfall, and hence flood risk. This in turn can
substantially increase the number of people exposed to highand extremely high risk from flooding (Tables 6 and 7). This
may directly affect the realisation of SDG 1 (e.g., Target
1.5 on building resilience to disasters), SDG 11 (e.g., Target 11.5 on reducing the adverse effects of disasters), SDG
13 (e.g., Target 13.1 on strengthening resilience and capacity to climate-related hazards and disasters), among others.
Meanwhile, different development strategies may influence
or intensify climate change to different levels.
Construction of key infrastructure, e.g., dams and reservoirs, may help better regulate water resources and protect
people from flooding damage, but it may also exert negative
impacts on the environment. All these will further complicate the issues and create challenges in evaluating the relationship between development strategies and SDG attainment, as well as the linkages between SDGs.

Moreover, the initial soil conditions for all of the scenario
simulations are assumed to be the same as those adopted forthe 2012 event. Although the soil conditions of the basin are
not expected to change significantly, the initial soil watercontent will be highly influenced by hydrological/weather
conditions before the flood event occurs and also land use.
But since LRB is a dry catchment and major precipitation
occurs in a short wet period of a year, it seems to be reasonable to assign infiltration parameters according to the soil
conditions following a dry season, as done in this work. In
the future, it is worth further investigating the effect of varying initial soil water content conditions on the flood simulation and risk analysis results.
Finally, due to the lack of relevant socio-economic data,
the potential flood losses cannot be quantified. But the simulation results are presented as accumulated inundated areas
against inundation depth, which can be directly coupled withsocio-economic data and vulnerability curves to estimate
flood losses when such information becomes available.

Limitations of this study

Conclusion

This work presents a new framework to systematically analyse basin-wide flood risk in connection to different development scenarios in LRB. The study is subject to several
limitations that need to be resolved in future work. First, theresolutions of different spatial datasets are not consistent.
For example, both the land use and the GPWv4 population
distribution data are at 1000 m resolution, which is resampled to 90 m to match the resolution of the adopted DEM tosupport flood modelling and the following analysis. This will
inevitably introduce uncertainties to the analysis and results.
Secondly, GPWv4 does not provide future projections, andthe population distribution data available for the baseline
year of 2015 is directly used in all of the analyses to identify
the at-risk population, assuming there is no change to the
population distribution from 2015 to 2030. The land usechange, especially the expansion of the built-up area, may
encourage redistribution and migration of population, andsuch a rough assumption may affect the accuracy of at-risk
population statistics. Therefore, the conclusions from thisanalysis may be conservative and should be used with care.
Furthermore, the adopted global climate dataset (NEXGDDP) has 1950–2005 as the retrospective period. But inthe current study, 2015 is adopted as the baseline year to
produce future development/land use scenarios. To obtainthe climate uplift factors against 2015, simple linear interpolation is implemented by assuming climate change affects
design rainfall linearly across time. The assumption may berough, and the climate projections and uplift factors should
be adjusted in the future if local climate data are available.

From the simulation and analysis results, the following conclusions are drawn:

13• Whilst the basin-wide inundated areas are predicted tobe similar under different land use change scenarios forthe extreme 100-year rainfall as considered in this work,
the inundated areas of individual land use types may varysignificantly, indicating different exposure to flood risk.
• The number of people at high risk of flooding is closely
linked to the distribution of the built-up areas, which
commonly accommodate dense population and arelocated in low-lying areas and floodplains that are more
vulnerable to flooding. All four land use change scenarios
project expanded built-up area, leading to increased floodrisk to the people.
• Climate change, especially the RCP8.5 climate scenario,
may lead to more frequent and intense rainfall, subsequently causing an increase in the total inundated areas
as well as the number of people at high and extremely
high risk of flooding.
• Large dams and reservoirs can substantially reduce inundation in the downstream areas close to the dam sites but
only have a limited impact on the total inundated areas
across the basin. Dams and reservoirs may provide critical protection to the downstream population and substantially reduce the number of people exposed to extremely
high risk. However, large dams and reservoirs may posean adverse impact on the environment and increase therisk of catastrophic dam-break floods.

Sustainability Science

It is evident from the modelling and analysis results thatflood risk at LRB is closely related to different development
strategies, which will affect the realisation of SDGs. It isessential to investigate and understand the inter-dependent
relationships between basin development strategies, floodrisk and realisation of SDGs to inform the development ofa suitable policy to ensure the sustainable future of LRB.
This will help minimise the trade-offs and maximise thesynergies between SDGs. The proposed basin-scale floodrisk assessment framework provides an important tool forthis purpose. Based on a physically based high-performancehydrodynamic flood model and open data, the framework
can be readily applied to other river basins, especially thosein developing countries where access to high-quality datafrom government agencies is limited.
Acknowledgements This research is funded by the Natural Environment Research Council (NERC) of the UK Research and Innovation(UKRI) through the Towards a Sustainable Earth (TaSE) programme(NE/S012427/1).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article areincluded in the article's Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view acopy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

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