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Geomatics, Natural Hazards and Risk
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Flood risk assessment in South Asia to prioritize
flood index insurance applications in Bihar, India
Karthikeyan Matheswaran, Niranga Alahacoon, Rajesh Pandey & Giriraj
Amarnath
To cite this article: Karthikeyan Matheswaran, Niranga Alahacoon, Rajesh Pandey & Giriraj
Amarnath (2019) Flood risk assessment in South Asia to prioritize flood index insurance
applications in Bihar, India, Geomatics, Natural Hazards and Risk, 10:1, 26-48, DOI:
10.1080/19475705.2018.1500495
To link to this article: https://doi.org/10.1080/19475705.2018.1500495
© 2018 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 25 Dec 2018.
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Flood risk assessment in South Asia to prioritize flood
index insurance applications in Bihar, India
Karthikeyan Matheswaran, Niranga Alahacoon, Rajesh Pandey and
Giriraj Amarnath
International Water Management Institute (IWMI), Colombo, Sri Lanka
ABSTRACT
Floods cause catastrophic destruction to life and livelihood in South
Asia than any other parts of the world. This research assessed long
term (2001 to 2015) flood risk at South Asia level using eight-day
Moderate Resolution Imaging Spectroradiometer data and subse-
quently expanded this methodology to identify potential zones for
piloting flood index insurance scheme in Bihar, India. Bihar was fur-
ther assessed for sub-regional segmentation of its 37 districts into
four flood risk zones based on k-means clustering and Getis-Ord Gi
analysis of multi-modal dataset consisting of demographics, meteoro-
logical, agricultural, flood characteristics and economic loss from
floods. Satellite based risk assessment identified parts of Indus basin
in Pakistan, Ganges basin in North India and majority of Bangladesh
as flood hotspots. Site prioritization for flood index insurance in Bihar
identified Madhubani, Darbhanga, Muzaffarpur and Sitamarhi districts
as very high flood risk districts by both methods signifying large
impact of any potential interventions implemented in these districts.
ARTICLE HISTORY
Received 20 December 2017
Revised 9 July 2018
Accepted 9 July 2018
KEYWORDS
Flood risk; index insurance;
South Asia; Bihar; site
prioritization
1. Introduction
Floods affect more population than any other weather-related peril. Floods accounts
for 47% of natural disasters occurred during the period 1995–2015 affecting 2.3 bil-
lion people worldwide and causing total economic loss of $662 billion (UNISDR and
CRED 2015). Globally 800 million people live in flood prone area with an average
annual exposure of 70 million (UNISDR 2011). Rising populations, socio-economic
development in flood prone areas and climate change will make 2 billion people vul-
nerable to flood disasters annually by 2050 (UNU 2004). Top 15 countries ranked on
number of people affected by river flooding accounted for nearly 80 percent of the
total population affected every year. Top flood prone counties lie in South Asia,
where largest exposed population to flood risk currently resides (World Bank 2015).
In South Asian countries, floods affected more than 1 billion people in past 20 years
(EMDAT 2015). India has 0.8% of its $1.87 trillion GDP exposed to flood risk and could
CONTACT Giriraj Amarnath [email protected]
ß 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://
creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any
medium, provided the original work is properly cited.
GEOMATICS, NATURAL HAZARDS AND RISK
2019, VOL. 10, NO. 1, 26–48
https://doi.org/10.1080/19475705.2018.1500495
increase more than 10-fold by 2030 (WRI 2015). The summary of catastrophic flood
events in the South Asian countries between 2004 and 2016 along with the economic
losses and insurance payouts are provided in Table 1. Global climate change is likely to
increase frequency and severity of flooding in South Asia (Turner and Annamalai 2012;
IPCC 2014) threatening agricultural production and increase uncertainty for small-scale
farmers whose livelihoods serve the rural economy in these regions necessitating an inte-
grated approach to overall risk reduction. Structural measures to confine floodwater
within pre-designated area are the most common mitigation strategies implemented in
South Asia to reduce damag es. However, the scale and magnitude of flood risk in South
Asia necessitates coherence in strategies combining investments i n structural measure
along with non-structural interventions to b uild resilience of vulnerable communities.
The integrated flood risk management strategies consisting of both structural and non-
structural measures need to based on long-term flood risk assessment at multiple scales
integrating cause–effect factors of flood events.
Application of free satellite data sources for flood mapping applications have
become imperative process as a part of disaster risk management. Possessing wide
spatio-temporal coverage, spectral band indices such as Normalized Difference Water
Index (NDWI) (McFeeters 1996) and the Land Surface Water Index (LSWI) (Xiao
et al. 2004) derived from Moderate Resolution Imaging Spectroradiometer (MODIS)
provide long-term coverage of flood pattern suitable for identifying hotspots.
Increased availability of satellite data and tools for flood risk monitoring used to
derive long-term flood patterns (Amarnath et al. 2017). When the satellite derived
spatio-temporally dense flood maps are integrated with data from secondary sources
on historical flood impacts, it is possible to classify regions into flood zones with
homogeneous characteristics of different risk level.
Regionalization of flood risk area involves spatial association of criteria/factors to
identify homogeneity in a multidimensional space (Booij 2005; Minea 2013; Xu et al.
2013). However, representation of multiple dimensions of vulnerability in the dataset
complicates quantifying of relative impact of these factors through single scorecard
(Rogelis et al. 2016). Often multi-criteria decision analysis methods are employed to
analyse an integrated dataset representing various bio-physical and socio-economic
Table 1. Major flood events, human and economic damages in South Asia (2005–2016).
Year Region Location
Lives
lost
Economic
loss ($ billion)
Insurance
settlement ($)
2004 Bangladesh 39 out of 64 districts 761 2.3 –
2005 India Mumbai 1094 0.70 400 million
2007 Bangladesh 46 out of 64 districts 1230 0.12 –
India Spread across different states 2051 0.38
Pakistan – 526 0.32
Nepal – 214 –
Sri Lanka – 33 –
2008 India Kosi 493 0.25 –
2010 Nepal Dadeldhura 98 0.3
Pakistan 1/5th of total land area 1700 9.7 –
2011 Sri Lanka – 65 0.5
2013 India Uttarakhand 4000 3.8 600 million
2015 India Chennai 315 3.0 330 million
2016 Sri Lanka – 245 2.0 107 million
Data aggregated from http://www.emdat.be/, www.reliefweb.int and Aon Benfield (2015).
GEOMATICS, NATURAL HAZARDS AND RISK 27
factors to an optimal decision (Hwang and Lin 1987; Ghanbarpour et al. 2013).
Chavoshi et al. (2012) and Rahmati et al. (2016) listed several multi-criteria decision
analysis methods previously used for regionalization of flood risk area. Cluster ana-
lysis is a one such approach where homogeneous groups of regions are created based
on a multidimensional space of various factors/criteria characteristics relating to flood
response (Tasker 1982; Bhaskar and O’Connor 1989). Several studies on clustering
techniques to identify flood risk zones with associated biophysical and socioeconomic
variables namely K-means for regional flood frequency analysis in Central India
(Burn and Goel 2000); comparison of hierarchical and Non-Hierarchical Clustering,
k-means, Fuzzy C-Means and Kohonen methods to delineate hydrologic homogenous
regions in Caspian Sea Watersheds (Chavoshi et al. 2012) and natural breaks classifi-
cation (Jenks), k-means, fuzzy c-mean and gaussian mixture model for potential flood
prone areas mapping (Papaioannou et al. 2015) in Xerias River watershed, Greece.
The selection of appropriate clustering method for delineating homogenous flood vul-
nerable regions targeting index insurance depends on availability of site-specific data-
set covering wide range of factors. Although studies advocate regionalization of flood
zones based on hydrographic boundaries, it is seldom possible in regions like South
Asia due to data availability at administrative levels and restriction on data sharing
mechanism in transboundary basins. In addition, selecting administrative units as the
basis for regionalization of flood risk area enables authorities to decide resource allo-
cation such as subsidies for insurance premium (Sanyal and Lu 2006).
International Water Management Institute (IWMI) is leading a project to design and
pilot meso-scale index insurance in flood prone regions for vulnerable agricultural com-
munities in two countries, India and Bangladesh. As such, no precedence or method-
ology exist in South Asia or other parts of developing world to identify homogenous
flood risk regions based on multidimensional space specifically collated for piloting flood
index insurance purposes. In response to the knowledge gap, this research study was
undertaken to assess a comprehensive methodology for regionalization of flood risk
zones and subsequent site prioritization among administrative units through application
of two clustering techniques namely k-means and Getis-Ord Gi
on multi-component
dataset from different sources (1985–2015). This study assessed flood risk at South Asia
level using long term (2001–2016) MODIS satellite based data to delineate regional flood
hotspots and subs equently applied two statistical approaches (K-means cluster and Getis-
Ord Gi
) to prioritize districts in one of the identified flood hotspot – Bihar, India into
risk categories for piloting flood index insurance. The prioritization approach applied in
this study is directly transferable to other states or sub-national areas in other countries
for prioritizing flood risk zones to pilot flood specific agricultural insurance schemes.
The outputs will aid to scale-up the flood specific insurance products from proposed
pilot area to other locations in Bihar associated with different risk factors.
2. Study area
Monsoonal climate, large rural population and dense agricultural activities in flood
plains affected by extreme flood events inflicting huge human and economic losses
dominate most of the South Asian countries. Bihar is an Indian state bounded by
28 K. MATHESWARAN ET AL.
Himalayan foothills and terai region of Nepal in the north and alluvial rich Indo-
Gangetic plains in the remaining direction between 24
20
0
10
00
Nto27
31
0
15
00
N and
83
19
0
50
00
Eto88
17
0
40
00
(Figure 1). The rainfall is dominated by south-west mon-
soon (June–September) which contributes 80% to 90% of the total rainfall received.
Table 2 provides a synoptic view of socio-hydrological indicators for Bihar. During
the monsoon season, river flows increases up to 50 times of summer flows causing
devastating floods. Average daily temperatures vary from 38
C in summer to 8
Cin
winter. In Bihar, 28 out of 38 districts are considered flood prone (of which 15 dis-
tricts are worst affected) causing huge loss of property, lives, farmlands and infra-
structure. As such, 73.63% of the geographical area of Bihar is considered to be
prone to floods. Agriculture is the backbone of Bihar’s economy covering 70% of its
geographical area, employing 76% of workforce and generating nearly 42% of the
Gross Domestic Product. Principal food crops are paddy, wheat, maize and pulses
(DoA 2015). During the 2008 Kosi floods, over 1416 km
2
of paddy, 72 km
2
of maize
and 971 km
2
of other crops were adversely affected, with affected population
of 500,000.
3. Materials and methods
3.1. Primary and secondary data sources
A composite time series dataset describing cause and effects of floods in Bihar was
aggregated at district level. This dataset contains factors influencing occurrence of
Figure 1. Location map of Bihar state, India with district boundaries and the major river networks.
GEOMATICS, NATURAL HAZARDS AND RISK 29
floods such as rainfall, impacts from floods such as inundated extent, specific impacts
on agriculture such as crop affected area and after effects of floods such as human
and economic damages. Most these dataset are time series data covering the period
1991–2013 sourced from multitude of sources. Name of the variable, time period of
availability and its data source is shown in Table 3. In total, 23 variables correspond-
ing to 37 districts were aggregated in the master dataset for further analysis.
3.2. Methodology
A comprehensive methodology for the entire process from data collection to assess-
ment using cluster analysis to identify inherent similarity among flood risk in the dis-
tricts is presented in Figure 2. The methodology involves utilizing 15 years of MODIS
eight-day surface reflectance data between 2000 and 2015 for mapping flood inunda-
tion extent for South Asia. Upon creating a flood frequency map from the long-term
MODIS data, the geographic focus of the study was further pruned to Bihar for pri-
oritizing the districts. The followed in regionalization of flood risk area in Bihar is
summarized in Figure 2. Two flood risk regionalization methods (i) cluster and (ii)
hot spot analysis methods were used to group districts in Bihar into four risk zones.
These two approaches were primarily selected for ease of application along with the
intention to transfer for other basins by other stakeholders in the future. From the
result of flood risk zonation, a district with large potential and impact was selected
for piloting a flood index insurance scheme.
3.2.1. Flood hazard mapping
IWMI flood mapping tool (Amarnath 2014; Amarnath and Rajah 2016) was used to
extract the inundated water pixels from MODIS (MOD09A1) eight-day composite
surface reflectance product with 500 m spatial resolution. IWMI flood mapping tool
is based on the method employed by Xiao et al. (2005) for mapping paddy fields
using inundation. In total, 645 8-day composite surface reflectance scenes covering
from 2000 to 2015 were downloaded for flood inundation mapping to discriminate
water-related pixels and non-flood pixels. The flood mapping algorithm uses land-
water indices consisting of Enhanced Vegetation Index (EVI), Land Surface Water
Table 2. Bihar – Key indicators.
Indicator Value
Total Population (2011) 103.8 million
Male 54.1 million
Female 49.6 million
Geographical area 94,163 km
2
Poverty Headcount Ratio (%) 29.80
Total number of poor (in millions) 35.50
Number of Districts 37
Annual average rainfall 1270 mm
Density of Population 1102 per km
2
Agricultural area 79,460 km
2
Avg. Number of Rainy Days 52.5 Days per Year
Contribution of Agriculture to GDP (%) 21.30
Gender Related Development Index (GDI) 0.6
Major flood events 1978, 1987, 1998, 2004, 2007, 2008 and 2016
30 K. MATHESWARAN ET AL.
Table 3. Variables included in delineating of flood risk zones.
Sl. No. Variable name Time period
Rainfall (mm) – Indian Meteorological Department (IMD) 0.25
o
gridded data
1 Monthly total 1991–2015
2 Monsoon total (Jun, Jul, Aug, Sep) 1991–2015
3 Yearly total 1991–2015
Inundation – Flood maps from 8-day MODIS surface reflectance data
4 Monthly Frequency 2001–2015
5 Monthly Area 2001–2015
Demographics (numbers) – Bihar Statistical Handbook 2012 (DES and DPD 2012)
6 Population total 2001 & 2011
7 Male 2001 & 2011
8 Female 2001 & 2011
9 Cultivator 2011
10 Laborers 2011
Flood impacts – Flood reports (FMISC, 2016)
11 Number of people affected by floods (yearly total) 1991–2013
12 Villages affected by floods (yearly total) 1991–2013
13 Houses affected by floods (yearly total) 1991–2013
14 Blocks affected by floods (yearly total) 1991–2013
Flood affected area (km
2
) - Flood reports (FMISC, 2016)
15 Total area affected (yearly total) 1991–2013
16 Agricultural affected area (yearly total) 1991–2013
17 Non-agricultural area (yearly total) 1991–2013
18 Crop affected area (yearly total) 1991–2013
Economic loss (lakh rupees) - Flood reports (FMISC, 2016)
19 Crop loss (yearly total) 1991–2013
20 House damage (yearly total) 1991–2013
21 Public property (yearly total) 1991–2013
Rice – Bihar statistical Handbook 2012 (DES and DPD 2012; Indiastat
22 Yield (t / ha) (yearly total) 2001–2009
23 Production (Mt) (yearly total) 2001–2009
Figure 2. Research methodology for regionalization and prioritization of flood risk areas for index
insurance product development.
GEOMATICS, NATURAL HAZARDS AND RISK 31
Index (LSWI) and DVEL representing difference between EVI and LSWI were com-
puted for every 8-day composite.
LSWI ¼
q
NIR
q
SWIR
q
NIR
þ q
SWIR
(1)
Where, q
NIR
is the reflectance of near infrared (841–875 nm, band 2), q
RED
is the
reflectance of red band (621–670 nm, band 1), q
BLUE
is the reflectance of blue band
(459–479 nm, band 3), q
SWIR
is the reflectance of short-wave infrared (1628–1652 nm,
band 6).
EVI ¼ G
q
NIR
q
RED
q
NIR
þ c
1
q
NIR
c
2
q
BLUE
þ L
(2)
Where, G is a gain factor, L is a canopy background adjustment factor, C
1
and C
2
are coefficient of the aerosol resistance term. The parameters for the EVI calculation
has define as G ¼ 2.5, L ¼ 1, C
1
¼ 6 and C
2
¼ 7.5
DVEL ¼ EVI LSWI (3)
Based on the above indices calculated by IWMI’s flood mapping algorithm, a pixel
is deemed to represent water when EVI values is less than or equal to 0.05 and the
LSWI value less than or equal to 0 or pixels which have the EVI values less than or
equal to 0.3 and DVEL values less than or equal to 0.05. Time series of flood inunda-
tion maps developed based on the above method for the period 2001 to 2015 were
aggregated to derive annual flood extent.
3.2.2. District prioritization approach
Two statistical methods (1) k-means clustering and (2) Hotspot analysis (Getis and
Ord 1992) were used for evaluate the site prioritized map of districts in Bihar.
K-mean clustering: The k-means is an unsupervised clustering method which
divides the dataset into a pre-determined number of clusters by iteratively minimizing
square error criterion expressing the distances between the data points and prototyp-
ical elements of a cluster (Hartigan and Wong 1979). The steps involved in kmeans
method to derive the flood risk zones consist of Decide on ‘k’ number of clusters rep-
resenting number of flood risk zones
Selection of random ‘k’ cluster centres
Estimation of the distance of each district to the k cluster centres, and transfer dis-
trict to nearest clusters based on the distance to the cluster centre
Calculate mean of districts within each cluster and assign k cluster centres to the
mean of their cluster.
Reassign districts closest to the new cluster centre
The above two steps are repeated until convergence of the objective function
defined below to a minimum. It is defined as
32 K. MATHESWARAN ET AL.
J ¼
X
k
i¼1
X
x2C
i
xc
i
jj
; (4)
where, k – number of clusters; x – district used in the classification; and c
i
is the
centre of cluster i that belongs to cluster C
i
.
Silhouette coefficient was used to estimate cluster quality (Everitt et al. 2011).
Silhouette coefficient represents the optimum number of clusters which signifies
homogeneity within the specified group.
s
i
¼
b
i
a
i
max a
i;
b
i
ðÞ
(5)
where a
i
– s the average distance of object i to all other objects in its cluster; for
object i and any cluster not containing it, calculate the average distance of the object
to all the objects in the given cluster and b
i
– the minimum such value with respect
to all clusters.
Optimum number of clusters for the data were determined based on the largest
magnitude of average silhouette coefficient for different number of clusters for the
given dataset. By selecting the number of clusters containing largest average silhouette
coefficient, the samples within the cluster were considered to be homogenous and
samples between different clusters represent heterogeneity. This will enable to assign
appropriate risk classes to the derived number of clusters.
HotSpotanalysis(Getis-OrdGi
statistic): The second method for flood hazard map-
ping involves two step process, hotspot analy sis with Getis-Ord Gi
statistics followed by
weighted overlay analysis. The Hot Spot Analysis tool in available in ArcMap 10.1 was
used to calculate the Getis-Ord Gi
statistic for each variable in a composite dataset.
Two statistical metrics z-score and p-value which are associated with standard normal
distribution to describe whether features with either high or low values cluster spatially.
The Gi
statistic returned for each feature in the dataset is a z-score. Smaller p-values
indicates that corresponding data is very unlike to have a spatial pattern whereas the
higher p-values represent larger contribution for the spatial pattern from the given par-
ameter. To be a statist ically signi ficant hot spot (district in this case), a feature will have
a high value and be surrounded by other features with high values as well.
G
i
¼
P
n
j¼1
w
i;j
x
j
X
P
n
j¼1
w
i;j
S
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
P
n
j¼1
w
2
i;j
P
n
j¼1
w
i;j
2
hi
n1
s
(6)
X ¼
P
n
j¼1
x
j
n
(7)
S ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
P
n
j¼1
x
2
j
n
s
X
ðÞ
2
(8)
GEOMATICS, NATURAL HAZARDS AND RISK 33
Where G
statics of z-score, x
j
is the attribute value for feature
j,
w
i,j
is the spatial
weight between feature i and
j
,nis equal to the total number of features. X and S
are the mean and standard deviation of the feature.
Only z-values were considered for the preparation of annual indicator map. Based
on selection criteria given in Table 4, three distinct ranks were assigned to districts
indicating similarity or variations for a particular variable from z-score. This process
is repeated for all the variables listed in Table 4 to derive z-score based rank for each
of 37 districts. Aggregated rank value for each variable was derived from sum of indi-
vidual yearly rank scores of the variable. The aggregated rank were further classified
in four flood risk zones such as low, moderate, high and Very high.
3.2.3. Data harmonization and processing
Since significant portion of data were collated from secondary sources, each of the
variables in composite dataset were evaluated for outliers to identify observations
which are deemed unlikely based on comparison with maximum, minimum, mean,
standard deviation and knowledge of realistically possible values. In particular, varia-
bles in ‘Flood Affected Area’ displayed significant anomalies upon employing outlier
detection methods. A comparison was made between flood affected area and geo-
graphical extent of the districts to determine unrealistic values. The analysis revealed
two years 1997 and 1999 where the values of three variables in flood affected area for
37 districts were found to be unreliable along with sporadic values in other time peri-
ods. These data were excluded from further analysis since large magnitude of varia-
bles tend to skew grouping patterns of districts in statistical analysis.
The corrected multi-variable district wise data consists of different units with abso-
lute value ranging from zero to tens of thousands. Dataset were normalized using
Equation (14) to standardize the existing range of independent variables in different
uinits present in master dataset.
X
0
¼
X
X
SD
(9)
Where X
0
– normalized value of the variable, X – Actual value of the variable,
X
SD
– Standard Deviation value of the variable range.
4. Results and Discussion
4.1. Spatio-temporal flood hazard analysis
The spatial extent of inundation from four major flood events (2004, 2007, 2008 and
2010) located geographically across South Asia can be visualized and assessed from
Table 4. Ranking criteria for zonation of districts
for flood risk regionalization based on z-score.
z-score criteria Rank
–1.6 1
>–1.6 AND <1.2 2
1.2 3
34 K. MATHESWARAN ET AL.
maps based on MODIS/TERRA data shown in Figure 3. The magnitude of flood haz-
ard from these individual major flood events can be deduced from the impact across
large swaths of area often extending across administrative boundaries between the
South Asian countries. The weekly flood inundation maps were aggregated into
monthly and annual flood inundation map to derive frequency of flooding in each
pixel across South Asia for the period of 15 years to produce flood recurrent map
(Figure 4). The flood recurrent map of South Asia show three main hotspots located
in Indus basin – Pakistan [Figure 4(A)], Ganges basin – east India [Figure 4(B)], and
Brahmaputra basin - Bangladesh [Figure 4(C)] respectively. These flood hotspots
Figure 3. Mapping spatial distribution of major flood events in South Asia (a) 2004 Bangladesh
flood; (b) 2007 Ganges flood in North India; (c) 2008 Kosi flood in Bihar, India and (d) 2010 Indus
flood in Pakistan.
GEOMATICS, NATURAL HAZARDS AND RISK 35
registered frequent occurrence of flood events within the 15 year mapping period.
High population density and intense agricultural activities in these regions coupled
with flood hazard increases the vulnerability of the inhabitants.
The flood hotspot in lower reaches of Ganges basin [Figure 4(B)] lies in the state
of Bihar, India was selected for regionalization of flood risk area based on district
boundaries. Nearly 46% of India’s flood prone area lies within ten states, of which
35% of area lies within top two states namely Uttar Pradesh and Bihar. The magni-
tude of flood hazard in Bihar can be deduced from the fact that total geographical
area of Uttar Pradesh is 2.5 times more than Bihar (94,163 km
2
) but both witness
Figure 4. Flood recurrent map of South Asia (2000 to 2015) and major flood hotspots in Pakistan
(A); Bihar, India (B) and Bangladesh (C).
36 K. MATHESWARAN ET AL.
similar annual average flood prone area (7 million ha). Bihar is the most flood
prone state in India with 55% of its geographical area affected by frequent flooding
[Figure 4(B)]. Assam, a relatively smaller state in North East India follows Bihar with
50% geographical area prone to flooding albeit followed by its larger neighbouring
states West Bengal and Uttar Pradesh with 37 and 32% respectively. The flood hazard
areas based on 15 years of MODIS flood maps along with three major flood events in
2004, 2007 and 2008 are shown in Figure 5. Bihar is divided into North and South
Bihar with the demarcation being River Ganges which cut across the state into two
halves. The MODIS flood hazard maps reiterates that Norths is at high risk of recur-
rent flood event compared to South Bihar, with the farmer being fed by upstream
heavy rainfall in Nepal Himalayas [Figures 5(a,b)]. In some years such as 2008 and
recent 2016 flooding, South Bihar was more affected than the North because of heavy
rainfall in catchment areas of small rivers flowing in the Southern part [Figure 5(c)].
Still the risk to South Bihar is significantly lower compared to North Bihar which is
evident in the aggregated flood maps [Figure 5(d)]
4.2. Validation of flood risk mapping
The yearly aggregated estimate of flood extents from MODIS was compared with offi-
cial estimates published by Bihar government in its statistical handbook (DES and
Figure 5. Mapping flood prone areas in Bihar using MODIS data for major flood events in (a)
2004, (b) 2007, (c) 2008 and (d) recurrent flood extent between 2001 and 2015.
GEOMATICS, NATURAL HAZARDS AND RISK 37
DPD 2012) for a period of ten years from 2001 to 2010. This yielded a reasonable
correlation of 0.63 (Figure 6). Spatio-temporal flood maps indicate that, on an aver-
age 15,000 km
2
of Bihar’ s geographical area was flooded every year with minimum of
7200 km
2
in 2010 and 25,000 km
2
in 2007. While there are appreciable difference
between MODIS and Bihar statistical handbook estimates of flood affected area par-
ticularly in 2002 and 2008, it must be noted that these official estimates are based on
aggregated reports from relevant administrative authorities on the ground before sat-
ellite based assessment were mooted by Flood Management Information Center
(FMISC), Bihar. The quantified dense spatio-temporal flood parameters from
MODIS/Terra based mapping are similar to the governmental estimates (FMISC
2016). Subsequently, the flood frequency and inundated area used for regionalization
of flood risk area in Bihar.
By overlaying the gridded population data onto the yearly flood maps derived
from MODIS, flood affected population were quantified (Figure 7). On an average 6
million people were affected by flooding each year, highest being 11 million people
from catastrophic 2007 flooding. There exists strong correlation (0.88) between flood
affected area and percentage of population affected by flood events. However unlike
the affected population estimates based on gridded secondary data, no such direct
relations were possible due to absence of similar dataset for crop loss. However, com-
parison between MODIS flood extent and crop loss information from DES and DPD
(2012), indicate that for similar flood affected area between 15,000 and 18,000 km
2
,
crop loss estimates could vary widely between 0.5 and 40 million USD. The flood
extent for the years 2001 and 2003 being nearly the same, observed crop loss wit-
nessed twofold increase. This indicate that the relation between flood extent doesn’t
necessarily translate into expected crop loss, rather the timing of occurrence play a
larger role in determining the extent of economic loss from agricultural activities.
Abovementioned findings have larger bearing on the provision of insurance and
affordability of the premium. By offering insurance only for predominant crop grow-
ing season which coincides with monsoonal flood events, the premium will be
Figure 6. Estimated flood extent from MODIS compared with Bihar statistical handbook areal
flood extent.
38 K. MATHESWARAN ET AL.
affordable for smallholders in conjunction with government subsidy while being
viable for insurance industry.
4.3. Flood risk regionalization of Bihar districts
K-means clustering were performed on the normalized dataset. Number of clusters to
classify the dataset ranged from two to 10 to determine optimum number of clusters
at which districts within individual cluster be similar and different from the districts
in other cluster. Average silhouette coefficient for corresponding cluster numbers
2–10 are shown in Figure 8. The average silhouette coefficient is largest (0.58) when
number of clusters are equal to 3. However, the difference in average silhouette coef-
ficient between cluster numbers 3 and 4 are minimal (0.05). Thus, the homogeneity
of districts within individual cluster can be effectively categorized in both the cluster
numbers (3 and 4). So it was decided to use four number of clusters to delineate 37
districts in Bihar into four risk zones: low, moderate, high and very high.
Zones delineated from kmeans consists of 12 districts in Zone 1 and Zone 2, four
districts in Zone 3 and eight districts in Zone 4. Out of four risk zones, three zones
(1, 3 and 4) consists entirely of districts which are in geographical proximity to each
other and located in three distinct river basins. Zone 1 consists of districts in South
Bihar away from the Ganges River, districts adjoining main channel of river Ganges
comprises Zone 2, South flowing rivers of Kosi basin forms Zone 3 and districts con-
taining tributaries of Ganges flowing from mountainous Nepal regions in North
Bihar forms Zone 4 (River Burhi Gandak, Bagmathi etc.). Three districts Kathihar,
Sheohar and West Champaran grouped in Zone 2 were further assessed due to geo-
graphical discontinuity with rest of the districts within the same cluster.
Figure 7. Estimated percentage of yearly flood extent from MODIS and corresponding percentage
of affected population in Bihar.
GEOMATICS, NATURAL HAZARDS AND RISK 39
Sheohar is the smallest district in Bihar in terms of geographical area (350 km
2
)
and are much smaller than average geographical area of remaining districts in the
same cluster due to which it is excluded from evaluation. Katihar and West
Champaran districts were further evaluated to assess whether the districts has to be
reassigned to other Zones. Two variables, monthly inundated area and total flood
affected area were used for evaluation of the zones to which these two districts were
assigned. The absolute value of two variables, monthly inundated area and total flood
affected area for West Champaran was compared with its corresponding values of
geographically adjacent neighbour East Champaran, which belongs to Zone 4. While
monthly inundated area for these two districts along the time period shows similar
peaks and trends, there exist substantial variation in yearly flood affected area
between these two districts. So the physical risk from flooding in these two districts
are inherently different which corresponds with the assignment to different zones
although geographically connected.
Using normalized dataset, mean value for each district encompassing all the varia-
bles were calculated and clustered according to the initial zonation results. Most of
the Normalized mean values of districts in Zone 1 are within 0.4–0.5 with a cluster-
wise average of 0.5 being lowest among four zones. No noticeable trends in district
wise normalized mean values in remaining three zones. However when cluster-wise
average is calculated for the remaining three zones, significant differences in the final
value of Zone 2, and 4 are noticeable. The normalized mean value for the initial clus-
tering output indicates insignificant difference between cluster 2 (0.62) and cluster 3
(0.64) signifying the existence of districts which can be interchanged between the two
clusters. However, after the evaluation and subsequent movement of Katihar to Zone
3 substantial difference is observed in the normalized mean values. Higher normal-
ized mean values of the cluster indicate overall large absolute values present in the
integrated dataset denoting probability of higher risk faced by the clustered districts.
The final flood risk zonation based on the evaluation and refinement is shown in
Figure 9.
Getis–Ord Gi
spatial statistical analysis was applied to individual variables in
Table 4 to categorize zones among Bihar districts. Rainfall distribution across districts
indicates that there exists high variability even within the districts of North Bihar.
Figure 8. Number of clusters and corresponding average silhouette coefficient.
40 K. MATHESWARAN ET AL.
North-east and North-West districts of Bihar witness high to very high rainfall along
with Muzaffarpur while the central districts of Darbhanga, Samastipur and Vaishali
receive moderate rainfall similar to their counterparts in South Bihar. The spatial pat-
terns in rainfall does not corresponds completely with spatial patterns in observed
flood inundation grouping. Maximum flood inundation hotspots lie in five districts
of Darbhanga, Muzaffarpur, Samastipur, Vaishali and Saharsa which are located in
North-Central portions of Bihar state. Flat slope, presence of dense network of river
channels and significant rainfall contribution from upstream Himalayan regions
transformed these five districts into high risk category. Five districts from North and
North-east Bihar located on the foothills of Himalayas along with Muzaffarpur,
Darbhanga and Saharsa suffered heavy damages to houses and villages. Ten districts
were clustered into very high economic loss category mainly from North Bihar while
four districts in North-Eastern part suffered moderate to high economic loss. Three
districts Muzaffarpur, Darbhanga and Sitamarhi consistently recorded large agricul-
tural damage and large number of population at risk due to flooding. The final risk
zonation map indicates that four districts Muzaffarpur, Darbanga, Sitamarhi and
Madubhani are considered to be high risk by Getis-Ord Gi
method (Figure 10). As
expected, none of the districts in South Bihar can be deemed as high risk category.
Districts of Ganges are deemed as moderate risks along with Araria and Purnia dis-
tricts in North Bihar.
Figure 9. Final zonation of 37 districts into four risk zones based on k-means cluster.
GEOMATICS, NATURAL HAZARDS AND RISK 41
4.4. Comparison of the k-means and Getis-Ord Gi
for flood risk regionalization
Comparison of flood hazard zones derived from the two applied statistical methods
are shown in Table 5. Of 37 districts classified into four flood risk zones, 25 districts
corresponds to the same risk classes in both k-means and Getis-Ord Gi
analysis.
Both methods delineated nine common districts in low risk category, eight districts
in moderate category, and four districts in high and very high risk categories (Figure
11). Both exploratory methods performed well in delineating, low and moderate risk
classes (with 90% accuracy) while deviated slightly (50–60% common districts) in
grouping high and very risk category districts. K-means clustered eight districts in
high risk category while Getis-Ord Gi
demarcated only four districts. East
Champaran, Khagaria, Samastipur and Saharasa grouped in very high risk category
by k-means method is classified into moderate risk zone by Getis-Ord Gi
. Most
often the districts which were not in agreement in both methods are classified to
nearest risk category. In spite of differences in final outcomes of risk categories, 68%
of districts were grouped under same classes in both methods. Except for two or
three districts, final regionalization maps from both methods indicate strong influence
of geographical proximity in deciding the individual group homogeneity which is in
contrary to the watershed based regionalization results by Chavosi et al. (2012) where
geographical closeness did not influence cluster homogeneity.
Figure 10. Final risk zonation of districts based on Getis-Ord Gi
analysis.
42 K. MATHESWARAN ET AL.
Table 5. Comparison of district Zonation outputs from k-means clustering and Getis-Ord Gi
analysis.
Flood risk zone
Low Moderate High Very high
Method k-mean Getis-Ord Gi
k-mean Getis-Ord Gi
k-mean Getis-Ord Gi
k-mean Getis-Ord Gi
Districts Aurangabad Aurangabad Araria Araria East Champaran
Banka Banka Bhagalpur Darbhanga Darbhanga
Bhagalpur Begusarai Begusarai East Champaran Khagaria
Buxar Buxar Bhojpur Bhojpur Katihar Katihar Madhubani Madhubani
Gaya Gaya Gopalganj Khagaria Muzaffarpur Muzaffarpur
Gopalganj Lakhisarai Kishanganj Kishanganj Saharsa
Jamui Jamui Munger Munger Madhepura Madhepura Samastipur
Jehanabad Jehanabad Nalanda Samastipur Sitamarhi Sitamarhi
Bhabua Bhabua Patna Patna Saharasa
Lakhisarai Purnia Purnia
Nalanda Saran Saran Sheohar
Nawada Nawada Sheohar Supaul Supaul
Rohtas Rohtas Siwan Siwan
Sheikhpura Sheikhpura Vaishali Vaishali
West champaran West champaran
Shaded cells indicate district grouped into same risk zone by both methods.
GEOMATICS, NATURAL HAZARDS AND RISK 43
Regionalization of floo d risk area using k-means and Getis-Ord Gi
integrated
predominantly public domain datasets available from the government publications
and repositories. Most of the Indian states pub lishes annual statistical abstract
which contains detailed data on disaster impacts. The results from regionalization
will allo w prioritization o f resources at regional level (d istrict) where further local
level assessments can be focussed for planning of mitigation measures, emergency
planning and vulnerability reduction (Kappes et al. 2012). Regional ization using
k-mean clustering contains low influ ence of subject ive weights, thus av oiding the
large variations influenced by expert’sperspective(M
€
uller et al. 2011). Application
of k-means and Getis-Ord Gi
enabled to reiterate and confirm risk cat egory of
70% of the districts within the s tudy domain emphasizing the use of multiple statis-
tical techniques to perform flood ri sk regionalization (Papaioannou et al. 2015)and
such prioritization based on only thematic layers can be dependabl e to maximum
extent (Chowdary et al . 2013).
Figure 12. Financial loss from crop damage due to floods for the period 2001–2012.
Figure 11. Number of districts within four flood risk zones identified from k-means, Getis-Ord Gi
and districts in same risk category in both methods.
44 K. MATHESWARAN ET AL.
4.5. Making a case for flood insurance in Bihar
Floods caused 324USD million worth of crop damages (2000–2012) to the agricultural
sector in Bihar which contributes 18.9% of Gross State Domestic Product but
employs 78% of the total population which is much higher than the national average
(Reference). Even during the relatively dry years of 2005–2006 and 2009–2012, 3 mil-
lion USD worth of average crop damage occurred due to minor flood events (Figure
12). These flood induced losses poses huge financial burden on the 33 million people
who depend on agriculture sector and living below the poverty line of 2 USD per day
(DoA 2015). Total economic loss due to flood induced crop damage for districts
located in very high and high zonation category during 2001–2012 is estimated as
$231 million and $64 million respectively (DES and DPD 2012). This financial loss
estimate is a conservative figure since not all the losses in these region are properly
documented owing to lack of proper monitoring and reporting mechanism.
Report of Task Force on agriculture estimated 43 billion USD of financial require-
ment from 2015 to 2022 for development of all aspects of agriculture in Bihar (Niti
Ayog 2015). Disaster management activities related to agriculture alone is earmarked
for 1.6 billion USD from total financial outlay within this period. However, majority
of this 1.6 billion USD is envisaged to be used as relief fund and input subsidy for
the affected farmers from disaster events. As a part of subsidized crop insurance
scheme for farmers across Bihar, 120 million USD is expected to be spent by the
administration in next seven years. In spite of all these valiant efforts, time gap in
insurance payouts for the suffered crop loss are not expected to be less than 10
months from current average of 16 months. In addition, no support is provided to
farmers abandoning farming activities due to waterlogging from flood events. This
indicate dearth of linkages between long term risk reduction and crop insurance
schemes in developing countries which needs to be addressed in design and imple-
mentation stage (Surminski and Oramas-Dorta 2013). Flood index insurance is cap-
able of addressing some of these issues in Bihar where potential exists for risk
transfer mechanism through operationalizing a parametric flood insurance as link
between short term coping mechanisms and long term risk reduction activities. As a
flood specific insurance product can be considered as associated product of the
Pradhan Mantri Fasal Bima Yojana (PMFBY), a pan India wide subsidized crop
insurance scheme to provide financial support to farmers suffering crop loss/damage
due to natural calamities/adverse weather conditions, to stabilize the income
of farmers.
5. Conclusions
The MODIS 8-day reflectance data was used to derive long term (2001–2015) spatio-
temporal flood patterns for South Asia. Three flood hotspots located in lower parts of
Indus basin in Pakistan, Gangetic plains in Bihar, India and Bangladesh regions were
identified to suffer from recurrent flood impacts. Two statistical exploratory methods,
k-means clustering and hot spot analysis were used to prioritize 37 districts in Bihar
state into four flood risk zones namely low, moderate, high and very high for flood
index insurance purposes. Overall 50% of districts grouped into flood hazard zones
GEOMATICS, NATURAL HAZARDS AND RISK 45
were in agreement in both statistical methods. Madhubani, Darbhanga, Muzaffarpur
and Sitamarhi districts are categorized as very high risk districts by both methods sig-
nifying any interventions or risk transfer mechanisms such as parametric flood insur-
ance introduced in these districts has the potential to mitigate on an average
minimum of $20 million per year economic losses suffered by smallholder farmer
communities. Muzzafarpur district, which is the closest high flood risk district to the
capital city Patna has been chosen to pilot flood index insurance for increasing the
climate resilience of smallholder farmers.
Acknowledgments
The authors would like to thank Flood Management Improvement Support Centre (FMISC)
and Disaster Management Department (DDM), Bihar for sharing relevant data for this study.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was made possible through the funding support of the CGIAR Research Program
(CRP) on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with
support from the CGIAR Trust Fund and through bilateral funding agreements. For details
please visit https://ccafs.cgiar.org/donors. We also thank the funding support from Japan’s
Ministry of Agriculture, Forestry and Fisheries (MAFF) and the CGIAR Research Program
(CRP) on Water, Land and Ecosystems (WLE), which is carried out with support from the
CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://
wle.cgiar.org/donors.
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