ASSESSING EXPOSURE AND VULNERABILITY

10 March, 2014

SUBMITTED TO: Danish Hydrologic Institute (DHI), Singapore

SUBMITTED BY: NEPAL DEVELOPMENT RESEARCH INSTITUTE Shree durbar tole, Pulchowk, Lalitpur INTRODUCTION 1

CHAPTER I. INTRODUCTION ...... 4 UNDERSTANDING OF THE TASK ...... 4 STUDY OBJECTIVES ...... 5 ROLE OF NDRI ...... 5 STUDY AREA ...... 6 CHAPTER II. ASSESSING EXPOSURE ...... 8 INTRODUCTION ...... 8 EXPOSURE DATASETS ...... 9 CHAPTER III. ASSESSING VULNERABILTY ...... 23 INTRODUCTION ...... 23 DAMAGE FUNCTION ...... 32 PEOPLE ...... 32 HOUSES ...... 40 AGRICULTURE DAMAGE ...... 41 ROAD ...... 42 INDUSTRIAL AREA ...... 44 REFRENCES ...... 45 ANNEX ...... 46

INTRODUCTION 2

LIST OF FIGURES AND TABLES

Figure 1: Extent and primary features of the Sunsari – Morang Irrigation Project (SMIP) ...... 4 Figure 2: Study Area ...... 6 Figure 3: Population density in the study area ...... 10 Figure 4: Gender ratio in the study area ...... 11 Figure 5: Households in the study area ...... 12 Figure 6: Household density in the study area ...... 13 Figure 7: Household size in the study area ...... 14 Figure 8: Land use map of the study area ...... 15 Figure 9: Settlements within SMIP command VDCs and at right and left banks of Saptakoshi River ...... 15 Figure 10: Land use map with settlements in the study area ...... 16 Figure 11: Road density based on ICIMOD geo-database ...... 19 Figure 12: Road density based on Department of Survey ...... 20 Figure 13: River density index of the study area ...... 21 Figure 14: Disadvantage group in the study area ...... 22 Figure 15: Direct Damage per Household in West Rapti River basin (NDRI, 2012) ...... 27 Figure 16: Yield in Metric tons per hectare for paddy in Morang and Sunsari district ...... 30 Figure 17: Percentage damage of paddy productivity in different flood conditions in West Rapti Basin .. 30 Figure 18: Survival rate of curves after submergence ...... 31 Figure 19: Damage functions to estimate percentage of rice crops yield loss due to flood ...... 31 Figure 20: Depth damage function of the study area ...... 36 Figure 21-a: Number of people losing lives and getting injured ...... 37 Figure 22: Depth damage function for different houses ...... 40 Figure 23: Depth Damage Curve for Yield Loss ...... 42 Figure 24: The depth damage curve for car ...... 43 Figure 25: Depth Damage for Industrial Area ...... 44

Table 1: Salient features of SMIP ...... 7 Table 2: Indicators for Exposure ...... 8 Table 3: Land use classification in SMIP VDC's...... 16 Table 4: List of Health Facilities in Morang District ...... 17 Table 5: List of Health Facilities in Sunsari District ...... 18 INTRODUCTION 3

Table 6: DAG Group ...... 22 Table 7: Flood inundation depth ...... 25 Table 8: Degree of Flood Hazard to People ...... 26 Table 9: Percentage distribution of HH by their foundation of house/housing unit...... 27 Table 10: Percentage distribution of HH by their outer wall of house/housing unit ...... 27 Table 11: Percentage distribution of HH by roof of house/housing unit ...... 27 Table 12: Direct damage function calculated for West Rapti River basin...... 28 Table 13: Paddy yield and its market value ...... 28 Table 14: Seasonal calendar for rice ...... 28 Table 15: Phenology of Paddy ...... 29 Table 16: Cropping pattern in Morang and Sunsari district (DADO, 2013/14) ...... 29 Table 17: Number of deaths and missing ...... 33 Table 18: Value of K(d) ...... 33 Table 19: Population density ...... 34 Table 20: Male to female ratio ...... 34 Table 21: Population of Morang and Sunsari district according to age group ...... 34 Table 22: Loss of lives and injuries to people by the flood in the study area ...... 35 Table 23: Estimation of Missing People ...... 36 Table 24: People killed and affected by disaster per year (2000-2001) by region ...... 37 Table 25: Depth- Damage Functions (Damage in NRs.) ...... 40 Table 26-a: Range of Yield Loss of Rice Crops due to Flood ...... 41 Table 27: Depth damage estimate according to crop stages ...... 42 Table 28: Damage function for road ...... 44 Table 29: Depth Damage for Industrial Area ...... 44

INTRODUCTION 4

CHAPTER I. INTRODUCTION

UNDERSTANDING OF THE TASK

Sapta Koshi River (also known as the Koshi) is renowned for its recurrent devastating flood events claiming a significant toll of lives and properties in the Terai plains of Nepal as well as in India. More notoriously known as the Sorrow of Bihar, Koshi frequently has been shifting its course over the course of time, and has inundated vast stretches of cultivable land as well as settlement areas. Shifting nature, unpredictable flows, high sediment load as well as the high dependence of the inhabitants on various water uses of this river have added further challenges to the overall water resources management in the Koshi River Basin. One of the major infra-structure in the Koshi Basin, the Sunsari - Morang Irrigation Project (SMIP) is one of the largest irrigation projects in Nepal which covers an approximate command area of about 68,000 ha in the Sunsari and Morang districts. It is one of the largest recipients of the waters from Koshi River. However, the cross-drainage and frequent flood across the Irrigation scheme have also caused significant damage to the project. The “beneficiary” (local inhabitants) has been more vulnerable to these frequent disasters. The extent of the land irrigated by the SMIP and some of the primary features of SMIP is illustrated in Figure 1.

Source: Inception Report Figure 1: Extent and primary features of the Sunsari – Morang Irrigation Project (SMIP) Against this backdrop, we understand that the sub-contract awarded to Nepal Development Research Institute (NDRI) for the mapping of exposure of important features and defining of vulnerability curves of INTRODUCTION 5 those features lying in the study area. It is an integral part of the DHI’s overall Risk Modelling in the Koshi Basin. STUDY OBJECTIVES The objectives of the study are: a. Establishing important features vulnerable to flood risks lying the study area and their mapping b. Defining vulnerability function for each selected feature i.e. preparation of depth damage curves. ROLE OF NDRI As a sub-contractor, NDRI will be involved, in line with the objectives, in the following sectors: a. Mapping of exposure data over the project area, which includes the SaptaKoshi flood-plain (left and right banks) between the Chatra Gorge and the Koshi Barrage, as well as the entire command area of the Sunsari Morang Irrigation Project (SMIP). The exposure information will involve spatial distribution of the human population and different types of property/assets over the project area. b. Definition of vulnerability curves for each exposure unit which relate the hazard, i.e. flood intensity e.g. inundation depth, to the losses associated with that level of hazard intensity.

INTRODUCTION 6

STUDY AREA

The study area under concern mainly covers the SaptaKoshi flood-plains (Saptari to the right and Sunsari on the left over banks) emerging from the Chatara Gorge till the Koshi Barrage within the Nepalese boundary, as well as the entire command area of Sunsari Morang Irrigation Project (SMIP) covering Morang and Sunsari districts (Figure 2).

Figure 2: Study Area Sunsari-Morang Irrigation Project: SMIP is one of the largest irrigation projects of Nepal that provides irrigation facilities for 6800 ha of arable land located in the heart of Sunsari and Morang districts in the Eastern Development Region. Run by Department of Irrigation (DOI) and as a high potential district for agricultural production, the project was developed to enhance the productivity of crops by irrigating these lands and also for river control and flood protection works on Koshi river especially in proximate to the Chatara Main Canal intake. The project was constructed by Government of India and has been handed over to government of Nepal in 1975. A road along the SMIP has also been constructed to ease farmer access to the markets for their products. The topography of the project is flat land where the elevation ranges from 60-107 masl. There are number of river and rivulets that crosses through Chatara Main Canal. The salient features of SMIP are illustrated in Table 1. INTRODUCTION 7

Table 1: Salient features of SMIP Command Area Drainage System Sunsari District 40,000 ha Total Length 825 Km Morang District 28,000 ha Head Reach Intake Total 68,000 ha Water Discharge Chatara Main Canal Head Reach at Main Canal Total Length 53 km Starting 45.3 cumex Super Passage Number 8 Present 60.0 cumex Aqueduct Number 36 Flushing Sluice Date 4 1 Syphon Number 2 Pre-Settling Basin 300 m Control Cross Regulator 16 nos Regulating Structure 1 Branch Canal Settling Basin 950 m x 60 m Total Length 332 km Sediment Discharge Dredger 2 nos Number of Canal 12 Hydro Power Plant 3.2 MW Secondary Canal 222 km New Intake 60 m Tertiary Canal 185 km Drop Structure 185 nos Drop Structure with bridge 74 nos Number of Bridge 32 Aqueduct Number 36 Source: NPC (2012)

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CHAPTER II. ASSESSING EXPOSURE

INTRODUCTION

Exposure is generally expressed in quantitative form where it is expressed in terms of number of people, assets and activities that are potentially threatened by a hazard. The current study aims to assess the vulnerability in terms of flooding disasters. Thus this study identifies the components that might be at risk in the flood hazardous area i.e. within the command area of SMIP and along the flood plains of the Sapta- Koshi River. According to Natural Hazard Risk Assessment report, the sectors deemed susceptible to the flood hazards triggered in the Terai region of Nepal basically include the followings: Primary affected sectors: the population residing in the region, housing, agriculture, health and education. Secondary affected sectors: industry, power, tourism, trade, irrigation infrastructure Others: real state, financial institutions The likelihood of the impact depends on degree and scale of the hazard for which a good database of the above mentioned sectors at a scale as small as possible is required. However, the smallest administrative units in Nepal are the wards for which the exposure information is rare to find and it will be very cost intensive process to collect the primary level of data. Above these units are Village Development Committees (VDCs) that lie under District Development Committee for which few level of necessary datasets are available for quantifying exposure. Although different elements have been recognized as exposure elements, only the major elements were considered, as illustrated in Table 2, in this study, i.e. population, housing and agriculture in defining the vulnerability curves for the study area. Information on health institutions is presented. Other information on elements like roads and drainage density as well as disadvantage group are also provided, as such information might assist in gaining perspective of the study area. Table 2 presents the indicators for each exposure feature (element) to probable flood hazard. Necessary data for assessing exposure were acquired from the secondary sources. A spatial layer for all the indicators identified has been created using ArcGIS 10.2.2. Table 2: Indicators for Exposure S.N. Type of Element of Indicator Sources Sectors Exposure 1. Primary Population a. Population density CBS, 2011 b. Gender ratio c. Age-group 2 Primary Households a. Households density CBS, 2011 3 Primary Agriculture a. Paddy* ICIMOD (land cover) 4 Primary Health Access to health institution MoHP, 2012/2013 5 Other Roads Density of road Department of Survey(DoS) 6 Other River River density ICIMOD 7 Other Marginalized group Disadvantages (DAG) group LGCDP * All the agricultural area is assumed to be of paddy field

Since some of the parameters (e.g. population density, household density) are to be expressed in terms of per unit area (square kilometer) basis; all of these information are created in a spatial layer. ASSESSING EXPOSURE 9

EXPOSURE DATASETS a. Population: Human causalities are expected to higher in the area with higher population density for a given flood hazard. It is expressed as the ratio of total population in the VDC to its total area. Comparatively women are said to be more susceptible to flood disasters. Gender ratio, the ratio of total male population to the total female population, of the study area for each VDC was also considered as one of the exposure parameters of the population. As an important exposure element to flood hazard, population data in this study was acquired from recent census data of Central Bureau of Statistics (CBS) of 2011. Total population has been extracted for Morang and Sunsari districts at the VDC level. This information covers the entire districts of Sunsari and Morang. Since gender also plays a crucial role in understanding vulnerability, gender classified (male and female) population is also acquired from the same source. Population was also classified according to age groups to assess the degree of vulnerability as children and old age people are considered more vulnerable. District level population data with an interval of five years age group was available for Morang and Sunsari districts. To disaggregate this data in VDC level, district level data was grouped into three category viz. Younger population (0-14 years), Active population (15-59 years) and older population (> 60 years). The number of people for each category of population in each VDC was calculated by multiplying its total population by district level proportion of each category.

Population density and gender ratio in each VDC are shown Figure 3 and 4 respectively.

ASSESSING EXPOSURE 10

Figure 3: Population density in the study area ASSESSING EXPOSURE 11

Figure 4: Gender ratio in the study area b. Households: Furthermore, total number of households in each VDC was also obtained from CBS data. Its density is obtained by dividing the total number of households in the VDC by the area covered by that VDC. A total number of households, household density and household size are shown in Figures 5, 6 and 7. ASSESSING EXPOSURE 12

Figure 5: Households in the study area

ASSESSING EXPOSURE 13

Figure 6: Household density in the study area ASSESSING EXPOSURE 14

Figure 7: Household size in the study area c. Agriculture: As the study area lies in the Eastern part of the country which is highly suitable for cultivation, majority of the population in the study area is reliant on agriculture and the recurrent floods in the area have created risk to these assets. Therefore to estimate the agriculture land that might be exposed to flood, a land use map of the study area was extracted from International Center of Integrated Mountain Development (ICIMOD) as shown in Figure 8. This base map is mainly based on MODIS imageries which have a spatial resolution of 250 Km x250 Km. Exposure to flood in terms of settlements in the study area was developed by digitizing these settlements using Google Earth Imagery of 2014 that lie within the SMIP command area. As a most prominent occupation in the area, agriculture land for each VDC was extracted after overlaying those digitized settlements in the Land cover map of 2010. A final land use map with the properties of settlements, agriculture land, forest, grass land and water bodies (rivers) was prepared. Figure 9 and Figure 10 shows the digitized image of settlements in Google Earth, Land cover map of the ICIMOD and final over laid map of the study area. ASSESSING EXPOSURE 15

Figure 8: Land use map of the study area

Figure 9: Settlements within SMIP command VDCs and at right and left banks of Saptakoshi River

ASSESSING EXPOSURE 16

Figure 10: Land use map with settlements in the study area Table 3: Land use classification in SMIP VDC's S.N. Land use type Morang Sunsari Area (Km2) % Area (Km2) % 1 Agriculture 492.28 84.66 729.33 74.67 2 Settlements 60.07 10.33 52.92 5.42 3 Forest 14.04 2.41 108.31 11.09 4 Bare area 10.24 1.76 59.20 6.06 5 Water Bodies 4.88 0.84 27.01 2.77 Total 581.50 100 976.77 100 Table 3 reflects the agriculture land as prominent area in terms of land use in both Sunsari and Morang SMIP covered area. Land use type in each VDC's of SMIP covered area are provided in GIS shape file. d. Health Human population access to health facility is one of the most important assets especially in any hazard event. Thus health in this section includes number of health facilities in the region. Health facilities in this study incorporates all level of government facilities i.e. Health post, Sub-health post, Primary Health Care Center, District Hospital and Zonal Hospitals available in each VDC of the respective administrative unit. The government level of health facilities data is accessed from Ministry of Health and Population (MoHP), 2012/2013. The health related information will be crucial to assess the vulnerability of the susceptible population towards the access to health services in the case of disasters. Table 4 and Table 5 represent the health facilities by their type in Morang and Sunsari district respectively. ASSESSING EXPOSURE 17

Table 4: List of Health Facilities in Morang District Morang VDC No. Morang VDC No. Zonal Hospital BIRATNAGAR SUB METROPOLITAN CITY 1 Health Post SANISCHARE Teaching Hospital BIRATNAGAR SUB METROPOLITAN CITY 1 SIJUWA Primary Health BAHUNI 7 SISABANIBADAHARA Center HARAICHA SUNDARPUR-MORANG JHORAHAT TANDI-MORANG JHURKIYA TANKISINUWARI LETANG TETARIYA URLABARI THALAHA Health Post BIRATNAGAR SUB METROPOLITAN CITY 34 Sub-health AMAHIBARIYATI 26 post BABIYABIRTA AMARDAHA BAIJANATHPUR AMGACHHI BANIGAMA MATIGACHHA BARADANGA BHOGATENI BAYARBAN HATHIMUDHA BELBARI JANTE-MORANG BHAUDAHA KATHAMAHA BUDHANAGAR KASENI-MORANG DADARBAIRIYA KEROUN DAINIYA MAHADEWA DANGIHAT MOTIPUR-MORANG DANGRAHA MRIGAULIYA DULARI NECHA GOVINDAPUR-MORANG PATIGAUN HASANDAHA POKHARIYA-MORANG HOKLABARI RAJGHAT-MORANG INDRAPUR RAMITEKHOLA ITAHARA SIDHARAHA KATAHARI SINHADEVISOMBARE KERABARI SISAWANIJAHADA LAKHANTARI SORABHAG MADHUMALLA TAKUWA MAJHARE TANDI-MORANG PATHARI WARANGI DRABESH YANGSHILA Total 69

ASSESSING EXPOSURE 18

Table 5: List of Health Facilities in Sunsari District Sunsari VDC No. Sunsari VDC No. District Hospital Inuruwa Municipality 1 Sub-health Post AEKAMBA 25 Teaching Hospital DHARAN MUNICIPALITY 1 AMADUWA Primary Health BARAHACHHETRA 5 AMAHIBELAHA Center HARINAGAR AURABARNI ITAHARI MUNICIPALITY BASANTAPUR-SUNSARI MADHUWAN BHADGAUSINAWARI SANTERJHORA BHALUWA Health Post BABIYA 22 BHOKRAHA BAKALAURI CHADWELA BHARAUL CHHITAHA RAMNAGARBHUTAHA CHIMDI BISHNUPADUKA DHARAN MUNICIPALITY DEWANGANJ GAUTAMPUR DUHABI HARIPUR DUMARAHA JALPAPUR DHUSKEE KAPTANGANJ HANSHPOSHA MADHESA INARUWA MUNICIPALITY NARSHINHATAPPU KHANAR PASCHIMKASUHA LAUKAHI RAMGANJSENUWARI MADHELEE SAHEBGANJ MADHYEHARSAHI SRIPURJABDI MAHENDRANAGAR SIMARIYA PAKALI SINGIYA PANCHAKANYA SONAPUR PRAKASHPUR PURBAKUSHAHA RAMGANJBELGACHHI TANAMUNA Total 54 e. Road The spatial road networks data of Nepal is differentiated into highway, railway, graveled road, metalled road, main trail and foot path according ICIMOD database. However, a government authorized agency Department of Survey has classified roads into different categories viz. District road, feeder roads, highway, other roads and roads under construction. This infrastructure is also a necessary asset to evaluate people’s access to basic needs. Both data sets are used in estimating the road density according the VDC of the study area. Road networks according to ICIMOD classification in this study, incorporates roads like highway, railway, metalled and graveled road and main trail excluding foot path (Figure 11). On the other hand, district road, highways and other roads are included for DoS based classification. To ASSESSING EXPOSURE 19 understand the road access in VDCs, road density in this study is calculated (Figure 12). Road density here is expressed as unit length of road in an administrative unit per unit area of that administrative unit, therefore is expressed in Km per Km2 of the area For creating a road density data, a spatial data of roads from ICIMOD was intersected with the VDC level spatial data. Then the length of the road was summarized according to VDC which was followed by joining the summarized data into projected VDC spatial data. Finally the final exposure data for roads according to the administrative units was exported to create the final layout.

Figure 11: Road density based on ICIMOD geo-database

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Figure 12: Road density based on Department of Survey f. River density The length of the river streams in each VDC is calculated to estimate the river density in each boundary. River density in this study area is expressed in kilometers per square kilometers of the area (Figure 13). The river data for the study area is extracted from ICIMOD geo-database. ASSESSING EXPOSURE 21

Figure 13: River density index of the study area g. Disadvantage group (DAG) Poverty is induced when people are deprived from economic opportunity which further leads to disadvantage when it is coupled with social opportunity and these groups are popularly known as disadvantage group (DAG). Hence these groups are defined as 'groups that are dependent for their livelihood upon daily-wage labor or occupational or other work that is barely sufficient to sustain daily needs; are landless or have some marginal land or have food sufficiency for less than six months a year; are unable to access minimum education, health and other services provided by the state; lack the confidence to voice even legitimate demands, and basic social, economic, political, religious and other rights; lack access to decision-making processes even within their community or at the local level; are excluded from participating in the developmental mainstream; and are socio-culturally excluded, subordinated and suppressed'.1 Nationally these groups have been categorized into four classes i.e. I, II, 3A & 3B, IV in their ascending order of concentration. These classes have been prepared based on composite index of seven indicators

1 http://daginfo.deprosc.org.np/ ASSESSING EXPOSURE 22 which are households (HHs)with food sufficiency less than 3 months; concentration of marginalized HHs; their access to primary schools; health posts; participation of women, Dalit and Janajati in planning, execution in decision making, prevalence of gender discrimination, prevalence of gender discrimination and prevalence of vulnerable households. Each indicators were ranked according to their severity and a final index was calculated which was then categorized into four classes (Table 6 and Figure 14). This category of data has been prepared for all 74 districts of Nepal and for each VDC and has been attained from Local Governance and Community Development Programme website.2 Table 6 presents number of VDCs for each category of DAG group in the study area. Table 6: DAG Group No of VDCs in district Category Type Morang Sunsari Saptari I Very Low Concentration of DAG 7 II Low Concentration of DAG 26 11 3A Medium Concentration of DAG 12 13 25 3B High Concentration of DAG 13 26 67 IV Very High Concentration of DAG 7 10 11

Figure 14: Disadvantage group in the study area

2http://lgcdp.gov.np/home/map-center.php

ASSESSING VULNERABILTY 23

CHAPTER III. ASSESSING VULNERABILTY

INTRODUCTION

There are different definitions of vulnerability from different disciplines. UNDRO (1991) has defined vulnerability as the degree of loss of a given element at risk or a set of such elements resulting from a natural phenomenon of a given magnitude and expressed on a scale of 0 (no damage) to 1 (total loss). Pelling (2003) however defines physical vulnerability as an exposure to risk and inability to avoid or absorb potential harm in the built environment that includes settlements, infrastructure and people's belonging. Vulnerability to flood is usually defined as measure of a region's susceptibility to flood damage (Hebb and Mortsch, 2007). Recently flood vulnerability does not only consider bio-physical factors but it is also linked with the social system that is associated with assessment of vulnerability to disasters (Chakraborty, et al., 2005). Therefore, vulnerability is shaped by incorporating physical, social, economic and environmental or ecological factors. Since this study is mainly focused on vulnerability to flood hazard, vulnerability is taken as a degree to which exposure elements (i.e. people, houses and paddy crop) is susceptible to harm (i.e. damage) due to the hazard (i.e. flood of certain return period). For estimating damage to exposure elements, flood hazard needs to be defined first. Flood hazard resulted from a flood of certain return period should incorporate flow velocity, depth and duration to give the completeness. However, to simplify the vulnerability analysis, on the assumptions that flow velocity and duration implicitly included in the depth of inundation, depth damage relationship is, generally, established to assess the vulnerability of the exposure element as mentioned in HAZUS user Manual and Nepal Hazrd Risk Assessment report (NHRA). The hazard map prepared for different return periods is, usually, based on the results of model simulation. The flood damage in the study area for a particular exposure element is, then, estimated based on vulnerability curves and hazard map for a given flood. Flood hazard maps are prepared in this study through hydraulic simulation study using MIKE 11. Vulnerability curves of exposure elements defined in this study are developed by NDRI. Vulnerability curve (damage - depth relation of the exposure element) should be based on historical damage-depth data. It is site specific as socio-economic values of the exposure element is different in different locations. It is noted that the level of analysis and the effort are highly related as shown in the Figure below:

Source: Modified from Hazus's study ASSESSING VULNERABILTY 24

It can, therefore, be developed only by intensive survey of the study area. For this stage of the study, proxy curves can assist to evaluate the CAPRA model supposed to be used in flood risk assessment. The vulnerability curves presented in this report are, thus, solely based on secondary data and literatures as well as assumptions. General overview of depth damage relationships Assessment of economic loss or damage is very essential for flood mitigation activities in any flood prone area. There is wide range of consequences that a flood incurs in different sectors like physical, social, psychological, environmental and economical and these has been categorized into tangible and intangible losses that might have direct and indirect damage for all of those categories. This damage is often estimated based on the property distribution in the inundated area. Two methods have been identified in conducting flood damage estimation. Firstly an extensive questionnaire survey is used to estimate the loss of lives and properties in the catastrophe area. Secondly stage damage functions are used to estimate the economic damage to different categories of assets for a given category of inundation depth and duration. These functions are derived either from past flood data analysis, or through analytical descriptions of flood damage to various properties considering the possible damage ratio to a given flood depth and duration (Herath, 2003). However, research regarding flood damage with respect to varying flood depth and flood duration in the context of Nepal is very sporadic. Nevertheless, a flood depth damage duration study was conducted in West Rapti basin by NDRI in 2012. This research was able to assess direct damage to sectors like house and property, agriculture land, Infrastructure and also indirect damage to small scale community level business. Different flood conditions and climate change scenarios were used to assess the damage to the sectors defined as above. Similarly, Nepal Hazard Risk Assessment conducted exposure, vulnerability and risk assessment (EVRA) study for different categories of disaster for seven rivers basins in Nepal viz. Bagmati, Kankai, Kamala, Rapti, Tinau, Babai and Narayani. For direct damage analysis different sectors like housing, education, health, agriculture and population were used. Though the flood hazard map for return periods of 10 years, 25 years, 100 years, 500 years were developed, the report addressed the exposure elements for frequent 10 year and extreme 100 years return period. In depth damage function, flood water depth is considered as a prime cause for direct damage. These functions relate to flood depth with the extent of economic damage that usually is the maximum possible damage in the flood prone area (Pistrika, 2010). The area of interest for this study consists of 82 VDCs that lie within the SMIP command area (DHI, 2013). Flood depth data according to NDRI, 20133 was categorized into four classes as given in Table 7.

3 NDRI, 2013. Disaster Risk Reduction and Climate Change Adaptation in Koshi River Basin Nepal. Nepal Development Research Institute, Pulchowk. ASSESSING VULNERABILTY 25

Table 7: Flood inundation depth Degree of Flood inundation Remarks severity depth Less affected < 0.5 m  Difficulty in general activities occur  Adult person can do movements easily and vehicle movement can take place to transfer the goods and people from the affected place to safer one  Even children and adult can survive the depth of this level and an average house can withstand this level of inundation without much damage.

Moderately 0.5 - 1 m  Movement of the people and vehicles become very difficult, i.e. only affected adult can go from one place to another with difficulty.  Household damage will be significant. People and animal can survive if it does not go beyond 1.0 m.  External rescue is needed during flood.

Highly affected 1 - 5 m  If the inundation depth goes beyond 1.0 m, no movement of people and vehicle is possible  Damage to properties is significantly high  Chance of survival of the people and animal is possible if the house is of two stories or more.  External rescue is needed during flood.

Severely > 5 m  Complete damage to properties takes place affected  Chance of survival of the people and animal is nil.  Air lifting is required even to rescue.

The number of injuries by flood hazard according to (Wallingford, 2006) was estimated as:

...... Equation 1 Where,

Ninj = Number of injuries within a particular hazard zone by the flood

Nz = Number of people within the hazard zone (at ground/basement level) HR = Flood hazard rating as a function of flood depth/velocity (within the hazard zone being considered) and debris factor; AV = Area Vulnerability as function of effectiveness of flood warning, speed of onset of flooding and nature of area (including types of buildings); and PR = People Vulnerability as function of presence of people who are very old and/ disabled/ long-term sick. The flood hazard rating, HR, is estimated as

...... Equation 2 Where, d = depth of flooding (m); v = velocity of floodwaters (m/sec); and ASSESSING VULNERABILTY 26

DF = debris factor (= 0, 1, 2 depending on probability that debris will lead to a significantly greater hazard The degree of flood hazard to people as a function of velocity and depth as given by Wallingford, (2006) is illustrated in Table 8. Table 8: Degree of Flood Hazard to People

d (v + 0.5) Degree of Flood Hazard Description <0.75 Low  Caution – "Flood zone with shallow flowing water or deep standing water" 0.75-1.25 Moderate  Dangerous for some (i.e. children) – " Danger: Flood zone with deep or fast flowing water" 1.25-2.5 Significant  Dangerous for most people – "Danger: Flood zone with deep fast flowing water" >2.5 Extreme  Dangerous for all – "Extreme danger: flood zone with deep fast flowing water" In disaster situation, women, particularly poor women suffer a great damage in terms of loss of life and property. Female disaster mortality rate is usually found to be higher than that of male (UNDP, 2013). Also women and children are14 times more likely than men to die during a disaster (Peterson, 2007 in UNDP, 2013). However, depending on the flood depth and duration and its damage to crops and buildings, following classes were defined to understand the degree of flood damage in the West Rapti basin4. Degree of Indicators Flood Flood affectedness depth duration Less affected Minor destruction, minimal damage of grain, damage < 30 cm < 1 day of standing crops, damage to newly planted crop, Moderately affected Cracking of the wall of houses, falling of wall, 30 - 70 1 - 3 days damage of stored food, sedimentation in the crop cm land Highly affected House completely or partially collapsed, loss of lives 70 cm > 3 days and livestock, inundation for longer period, people suffering from water borne diseases Depth damage functions and its economic values estimated for settlements and agriculture land are discussed below. It was, mainly, based on the research conducted in West Rapti Basin by NDRI. a. Damage to Settlements Damage to settlements is highly correlated to the attributes like construction material used for building walls, floors, roof of the house, stories etc. Information like these is usually covered through questionnaire survey in the flood prone area for best estimation of damages. However this study covers this information from CBS census of 20115 which is only available at district level. Table 9, 10, 11 clearly indicates settlements susceptible to flood where majority of households have their foundation, outer wall and roof as

4 NDRI, 2012. Assessment of Flood Inundation in Lower West Rapti Basin under the effect of Climate Change 5 CBS, 2011. National Population and Housing Census. Central Bureau of Statistics, Kathmandu, Nepal. ASSESSING VULNERABILTY 27 wooden pillar, Bamboo and Galvanized iron respectively. This data also corresponds to the survey conducted by NDRI in 2012 in VDC's of Sunsari and Spatari district where approximately 7 in 10 households have walls made up of bamboo/wood/branches followed by un-burnt bricks with mud. Table 9: Percentage distribution of HH by their foundation of house/housing unit Mud bonded Cement bonded RCC with District bricks/stone bricks/stone pillar Wooden pillar Other Morang 4.74 21.18 11.10 49.52 12.76 Sunsari 5.05 23.31 17.54 42.32 10.92 Saptari 7.09 18.74 3.53 65.06 3.72

Table 10: Percentage distribution of HH by their outer wall of house/housing unit Mud bonded Cement bonded District bricks/stone bricks/stone Wood/planks Bamboo Unbaked Others Morang 2.97 30.38 9.80 55.28 0.10 0.72 Sunsari 2.51 35.26 5.69 54.57 0.10 1.00 Saptari 5.24 20.95 1.25 69.39 0.20 0.90

Table 11: Percentage distribution of HH by roof of house/housing unit District Thatch/Straw Galvanized iron Tile/Slate RCC Wood/ planks Mud Others Morang 21.04 57.59 2.83 17.22 0.27 0.00 0.17 Sunsari 16.94 59.24 0.48 21.69 0.32 0.00 0.33 Saptari 51.60 22.89 11.24 11.70 0.51 0.00 0.19

Hence assuming a one storey house and with weak foundation, wall and roof material in the rural areas defined as VDC's other than Municipalities, we assume a following damage function defined in West Rapti basin (Figure 15 and Table 12). However, it is better to get the economic values of these types of household from questionnaire survey for the current study area. Though both area lies in Terai region and have majority of household sharing similar characteristic of house type, losses in terms of economic value might differ from region to region.

Figure 15: Direct Damage per Household in West Rapti River basin (NDRI, 2012) ASSESSING VULNERABILTY 28

Table 12: Direct damage function calculated for West Rapti River basin S.N. Flood depth Average damage in NRs per household 1 Less than 1 ft (< 0.305m) 13370 2 1 ft - 2 ft (0.305m -061 m) 22094 3 2 ft - 3 ft (0.61m - 0.91 m) 42620 4 > 3 ft (>0.91m) 113967 b. Damage to Agriculture land Damage to agriculture land is estimated on the basis of area inundated of the total cultivable land area. Floods in the study area occur mainly during monsoon season, hence crop especially paddy grown during this season will only be considered in this study. Paddy being the major staple food in the area which is grown twice every year (DADO, 2013/2014)6 and are sometimes grown thrice a year (DHI, 2013)7. There are several varieties of paddy cultivated in the area. DADO reports of Sunsari reported several varieties like Mansuli, Rampur Mansuli, Radha-12, Radha-17, Sona Mansuli, Suwarna, Pusha Basmati, C.N.T.L.R Panjabi, Sugandha, Loktantara, Mishrit and Ranjita in monsoon season while other local varieties were also reported. The average yield for paddy based on time series data in Sunsari district (1976- 2013) is 2.53 Mt/ha while the yield value can be even higher ranging 3.5-5.5 Mt/ha for rice variety Radha-12 and 4-5 Mt/ha for Radha-17. After the construction of SMIP canal, paddy yield within the SMIP command VDC's has been reported to increase significantly (NPC, 2012) compared to other VDC's in Morang and Sunsari district. Figure 16 also clearly indicates a rising trend for paddy. A primary level of data collected from the field could provide precise yield and market values of paddy in the study area. A yield value in Table 13 represents the maximum annual yield for a particular year in the respective districts (see Annex for yield values) with their price from the main market hub i.e. Biratnagar Municipality for the study area. Table 13 will be helpful in determining maximum possible economic damage as a replacement cost for paddy. A seasonal calendar and cropping pattern for monsoon rice as per the DADO reports of 2012/2013 is shown in Table 14 and Table 16. Table 15 indicates phenology of paddy for crops like Mansuli, Sabitri, Prithivi, Ramdhan, Loktantra, and Hardinath which has a yield potential of 5-6 t /ha. Table 13: Paddy yield and its market value Market Value of Crop Yield Market Value of Rice Market Value of Rice District Crop (Rs/Kg) Rs/Mt (Mt/ha) (Rs/Ha) (Rs/Km2) Morang 29.15 29147 3.55 103470.67 1034.706 Sunsari 29.15 29147 3.06 89188.80 891.888 Table 14: Seasonal calendar for rice

Growing Period Cropping calendar Sowing/Planting Harvesting District (Months) Morang Early/Spring Rice Feb/Mar; Mar/Apr Jun/Jul; Jul/Aug 4-5months Monsoon Rice Jun/Jul; Jul/Aug Oct/Nov; Nov/Dec 4-5 months Sunsari Early/Spring Rice Feb/Mar; Mar/Apr May/Jun; Jun/Jul 4-5 months

6 DADO, 2013/2014. Annual report on Agriculture Development Program. District Agriculture Development Office (Morang & Sunsari) 7 DHI, 2013. Koshi Basin Risk Modelling: Inception Report (unpublished) ASSESSING VULNERABILTY 29

Monsoon Rice May/Jun, Jun/Jul, Jul/Aug Sept/Oct, Oct/Nov, Nov/Dec 5 months

Table 15: Phenology of Paddy Stages Days Planting Time Nursery Raising: June 15-June 30 Transplanting- 21-25 days seedling Sprouting 1-3 days Emergence 3-5 days Transplanting 21-25 days Stand establishment 10 days Tillering 30 DAT (Day After Transplanting) Panicle Initiation 60-65 DAT Booting 70-80 DAT Heading 85-95 DAT Flowering 95-105 DAT Milking 10 days Grain filling 20-25 days Physiological maturity 150 days Table 16: Cropping pattern in Morang and Sunsari district (DADO, 2013/14) S.N. Sunsari Morang Saptari (in Irrigated Land)* 1 Paddy-Paddy-Wheat Chaite rice/Monsoon rice/Wheat Paddy-Paddy-Potato 2 Paddy-Paddy-Oil seeds Paddy/Wheat/Dhaicha Paddy-Paddy-Wheat 3 Paddy-Paddy-Maize Paddy/Wheat/Pulses(Mung) Paddy-Potato-Barren 4 Paddy-Paddy-Barren Paddy/Mustard/Maize Paddy-Wheat-Pulses 5 Paddy-Pulses-Barren Paddy/Masuro (pulses) Paddy-Wheat-Chaite Rice 6 Paddy-Vegetables-Vegetables Paddy/gram Paddy-Vegetables 7 Paddy-Oil seeds-Maize Paddy/Jute Paddy-Wheat-Pulses 8 Paddy-Wheat-Pulses Paddy/Maize Paddy-Wheat-Barren 9 Jute-Oil seeds- Barren Paddy/Mustard/Maize Paddy-Onion-Barren 10 Paddy-Barren-Barren 11 Maize-Oil seeds- Barren 12 Maize-Potato-Vegetables 13 Paddy-Potato-Maize * Based on DADO report of Saptari (2067/2068)

ASSESSING VULNERABILTY 30

4.00 Paddy 3.50

3.00

2.50

2.00

Yield, M.t/Ha Yield, 1.50

1.00 Morang Sunsari 0.50

0.00

Figure 16: Yield in Metric tons per hectare for paddy in Morang and Sunsari district In West Rapti basin, with the viewpoint of farmers, the height of paddy was considered to be 30cm (1ft) during flooding season. Based on this, following depth damage (Figure 17) curve was generated for different flood duration.

Figure 17: Percentage damage of paddy productivity in different flood conditions in West Rapti Basin NHRA have developed a vulnerability curve for paddy crop considering stages of growth and for their survival rates (Figure 18). A depth damage curves for rice has been developed by Shrestha et al. (2014) considering the different growing stages of rice, flood inundation depth and duration (Figure 19). ASSESSING VULNERABILTY 31

Figure 18: Survival rate of curves after submergence

Figure 19: Damage functions to estimate percentage of rice crops yield loss due to flood ASSESSING VULNERABILTY 32

DAMAGE FUNCTION

Proposed damage function for the study area Damage to properties and lives depends on technological advancement e.g. early warning system, consciousness of the people, availability of physical infrastructural like roads, evacuation places, economic conditions etc. As discussed above, to get the real picture of the flood damage function, comprehensive data collection in field level within the study area is required. Still it changes with time. However, an attempt was made to estimate such losses to provide the basis of using the model (Level 1).

PEOPLE The loss of lives and injuries to the people incurred by the flood per unit area is estimated based on Wallingford (2006) and NDRI (2013) studies and with the following assumption and available data. Assumptions: 1. Loss of lives or injuries to people by flood is directly related to the population density. 2. Loss of lives or injuries to people by flood is inversely related to the male female ratio (basis: UNDP, 2013). 3. Loss of lives or injuries to people by flood is related to the percentage of children and old people (<14 years and > 65 years), who are considered more vulnerable, to the total population. Demographic data of Sunsari and Morang are considered for this purpose (Basis: Wallingford, 2006; NDRI, 2013). 4. Since separate data of injuries, missing and deaths for flood is not available at study level, the ratio in the study area was estimated based on most recent national level data for floods of 2011, 2012 and 2013 (DWIDP, 2012, 2013 and 2014).

Based on above assumption, the modified form of Wallingford (2006) damage function for loss of lives can be of expressed in the equation form as:

...... Equation 3

Where,

= loss of lives (number/sq. km.) = population density (number/sq. km.) = ratio of male to female population = vulnerable population ratio (ratio = [children (<10 years) + old age (>= 65years of age)]/ [the total population]; Range: 0.00-1.00) K(d) = coefficient that depends on the depth of flow a, b and k1 are constants and depend on location.

~ Value of K(d) The number of deaths and missing people reported in (NCVST, 2009) for Sarlahi, Rautahat and Kalali (Terai district resembling the study area) are taken as loss of lives among the affected people. The flood is assumed to be of 1-5 m. The flood event of 2008 caused the death of 4-6 persons, displaced 40,378 persons from 7102 families in Nepal (ICIMOD, 2008). In India, the devastation was much bigger claiming ASSESSING VULNERABILTY 33

42 lives with more than 70,000 displaced people (ICIMOD, 2008). It shows that about 48 people lost their lives with affected people of 110,000. The percentage of dead people therefore, becomes 0.043%. The data shows that the loss of lives in Sarlahi district is exceptionally high and excluded. The flow condition might be significantly very high for such devastation. The percentage of people losing their lives as a percentage of affected people is estimated as 0.06% (Table 17). Table 17: Number of deaths and missing Districts Affected Death Year Loss in % Sarlahi 91110 687 1993 0.754 Rautahat 89146 111 1993 0.125 Kailali 158663 34 2008 0.021 Koshi 110378 48 2008 0.043 Total (Sarlahi excluded) 0.060 % of loss of lives in average 0.06 The assumed K(d) values are estimated as a product of K value (assumed value in comparison to base value of 1, for the flow depth of 1.0-5.0 m) and 0.06% as given in Table 18 for various depths. The degree of risk by flood hazard to people as illustrated by Wallingford (2006), indicate that risk factor increases by a order of 10, when depth increases in linear fashion. With this assumption, values of k are prescribed as given in Table 18.

Table 188: Value of K(d) Range (m) Median value K K(d) of depth (m) < 0.5 0.25 0 0.00000 0.5-1.0 0.75 0.1 0.00006 1.0 - 5.0 3 1 0.00060 > 5.0 7.5 10 0.00600

~ Value of k1 During the flood of 2008, loss of lives (deaths + missing) in Kailali district was reported to be 34 (NCVST, 2009). Out of them 13 were identified as male and 21 as female. Even from this data it can be surmised that female are more vulnerable than male to flood hazard. It shows that female population is 25% more vulnerable (average to be 17, loss is 21). Based on this data, k1 is assumed to take a value of 1.25. ~ Coefficients a For simplicity and not having real data to estimate its value, the value of coefficient "a" is assumed to be 1.0. ~ Coefficients b There is no real data to estimate the value of "b" as a. However, if the percentage of vulnerable people's ratio gets higher, there are less people to help them during the process of rescuing during flood. Therefore, time required to rescue those vulnerable people becomes more when there is high probability of more depth. It is, therefore, rational to assume that the deaths and/or injuries will be more if the ratio gets higher. To account the assumed non linear nature resulted from these considerations; the value of b is kept as 1.1, although close to 1 in the analysis in this study. ASSESSING VULNERABILTY 34

~ Estimation of population density (p) for the study area The land area of Sunsari and Morang districts and the total population are given in Table 19. Based on these data the population density of Morang and Sunsari districts, based on 2011 census, are respectively 520 and 607 person/km2. The average value of the density is calculated as 556 people/km2. This figure is taken as the value of p in this study. Table 19: Population density

District Area (km2) Population Density (people/km2) Morang 1855 965370 520 Sunsari 1257 763487 607 Total 3112 1728857 556 ~ The male female ratio, r The male female ration of the two districts and their ratio are given in Table 20. The average ratio of 0.94 is used in this study. Table 20: Male to female ratio

District Male Female Male Female ratio Morang 466,712 498,658 0.936 Sunsari 371,229 392,258 0.946 Total 837,941 890,916 0.94 ~ The vulnerable people, v The population of Morang and Sunsari of various age groups are given in Table 21. People are considered vulnerable if they are below 10 years and more than 65 years (NDRI (2013). Based on this basis the vulnerable people in Morang and Sunsari district come to be 24.7% and 25%. Thus the value of v in this study was taken as 0.25. Table 21: Population of Morang and Sunsari district according to age group

Morang Sunsari Number Percent Number Percent All Ages 965370 100 All Ages 763487 100 00-04 Years 85141 8.82 00-04 Years 68052 8.91 05-09 Years 102809 10.65 05-09 Years 86542 11.34 10-14 Years 115924 12.01 10-14 Years 95784 12.55 15-19 Years 106155 11.00 15-19 Years 84497 11.07 20-24 Years 86135 8.92 20-24 Years 70691 9.26 25-29 Years 78940 8.18 25-29 Years 64797 8.49 30-34 Years 68631 7.11 30-34 Years 54936 7.20 35-39 Years 63444 6.57 35-39 Years 50755 6.65 40-44 Years 55648 5.76 40-44 Years 42363 5.55 45-49 Years 47967 4.97 45-49 Years 34813 4.56 50-54 Years 41061 4.25 50-54 Years 29635 3.88 55-59 Years 33879 3.51 55-59 Years 23505 3.08 ASSESSING VULNERABILTY 35

Morang Sunsari Number Percent Number Percent All Ages 965370 100 All Ages 763487 100 60-64 Years 28951 3.00 60-64 Years 20904 2.74 65-69 Years 20637 2.14 65-69 Years 14525 1.90 70-74 Years 13705 1.42 70-74 Years 10138 1.33 75-79 Years 8179 0.85 75-79 Years 5757 0.75 80-84 Years 4585 0.47 80-84 Years 3273 0.43 85-89 Years 2131 0.22 85-89 Years 1465 0.19 90-94 Years 925 0.10 90-94 Years 650 0.09 95+ Years 523 0.05 95+ Years 405 0.05 Vulnerable population % 24.72 24.99

~ The loss of lives of people (Ploss) The loss of lives of people by flood thus estimated is given in Table 22. The depth damage function is presented in Figure 19. Table 22: Loss of lives and injuries to people by the flood in the study area

Loss of lives of and injuries Parameters p 556 person/sq. km

Depth (m) K(d) Ploss Pinjured r 0.94 0.00 0.00000 0.00 0.00 v 0.25 0.25 0.00000 0.00 0.00 Coefficients 0.75 0.00006 0.06 0.02 k1 1.25 3.00 0.00060 0.57 0.23 a 1 b 1.1 7.50 0.00600 5.67 2.32

ASSESSING VULNERABILTY 36

Ploss Pinjured

6 2

5

4

3

2

1

Loss of Lives ofLives Loss Injuuries/andkm 0 0.00 0.25 0.75 3.00 7.50 Depth (m)

Figure 20: Depth damage function of the study area Estimation of Injured People Reports on Disaster Review by DWIDP (in the year 2012, 2013, and 2014) provided the statistics of death, missing and injuries incurred by flood in 2011, 2012 and 2013 (Table 23), which is a national level statistics of Nepal. Death and missing were categorized to estimate loss of lives. Based on the statistics in Table 23, the injuries ratio to this loss is calculated as 0.082. By examining the data on loss and injuries, the injuries figures, one can easily conclude that these figures are of major injuries. The total injured person is thus assumed to be at least 5 times of this ratio. However, it is noted here that Wallingford (2006) estimates are quite high for injuries as compared to deaths. Based on these assumptions and basis, the missing people by flood is estimated and presented in Table 23 and Figure 20. Table 23: Estimation of Missing People Year Death Missing Injuries Death + Missing 2011 31 38 5 69 2012 52 39 8 91 2013 137 132 22 269 Total 220 209 35 429 Ratio of Injuries with death and missing = 0.082 Source: DWIDP (2012, 2013, 1014) A region wise data base of people killed by disasters per year, provided by UNDP (2013), is presented in Table 24. The people killed as a percentage of people affected are given in Col 4 of the table. If the affected population were 556 (which is the density of population of the study area per sq. km), the number of people losing their lives is calculated and given in Col. 5. The predicted value of the loss of lives by equation (1), for the flood depth of 1-5m (average 3m), is 0.57 (Col. 6). Although this results covers all the disasters and vast geographical regions, our prediction lies within the range given by UNDP (2013). It is thus concluded that for initial purpose the model proposed in this study is quite sufficient to estimate the depth damage for people.

ASSESSING VULNERABILTY 37

Table 24: People killed and affected by disaster per year (2000-2001) by region Region Killed Affected % of Loss of lives Model prediction (number per (in thousand per killed people per sq. (number (assumed depth annum) annum) per sq. km.) 1-5 m with mean 3m)

Col 1 Col 2 Col 3 Col 4 Col 5 Col 6 Africa 5874 14919 0.039 0.22 0.57 North America 427 2069 0.021 0.11 0.57 Asia-Pacific 73252 204350 0.036 0.20 0.57 Europe 8114 539 1.505 8.37 0.57 Latin America 24650 5805 0.425 2.36 0.57 and the Caribbean Source: UNDP (2013)

Col 4 = Col 2/Col3*100 Col 5 = 556 * Col4 /100 Example 1: Base Case

p 556 person/sq. km. r 0.94 v 0.25 Area of VDC 5 sq. km.

Figure 21-a: Number of people losing lives and getting injured

ASSESSING VULNERABILTY 38

Example 2: Density of population low p 400 person/sq. km. r 0.94 v 0.25 Area of VDC 5 sq. km.

Figure 21-b: Number of people losing lives and getting injured

Example 3: Male female Ratio = 1

p 556 person/sq. km. r 1.00 v 0.25 Area of VDC 5 sq. km.

Figure 21-c:: Number of people losing lives and getting injured

ASSESSING VULNERABILTY 39

Example 4: More Male than Female

p 556 person/sq. km. r 1.05 v 0.25 Area of VDC 5 sq. km.

Figure 21-d: Number of people losing lives and getting injured

Example 5: Vulnerable population less

p 556 person/sq. km. r 0.94 v 0.20 Area of VDC 5 sq. km.

Figure 21-e: Number of people losing lives and getting injured

ASSESSING VULNERABILTY 40

HOUSES The damage of house due to flood are based on the following assumption and available data. Assumptions: 1. The damage of house by a given depth of flood depends on the types of foundation, wall and the roof. Foundation, wall and roof of a house, even the content within the house, depend on the economic condition of the house owner and how he uses the house. Therefore, the economic condition directly relates to the quality of construction material used for building a house. Hence, it can be considered that roof type gives the complete description of its value for a given house. 2. The loss at the study site resembles to the loss estimated at West Rapti River Basin. 3. The houses are slightly better in east than in west because of higher pace of development in eastern part of Nepal. Material content in the house can be expected to be more and thus the consequent loss of those assets. When there is no data in hand to account these factors, flood damage is kept 10% more expensive in the study area than that in the West Rapti Basin (Regional effect: east-west). The cost of the asset is being increased by10% annually (inflation effect). 4. All house types are distributed equally. Based on above assumptions, Table 25 and Figure 22 gives the depth damage function for different houses. Table 25: Depth- Damage Functions (Damage in NRs.) S.N. Depth (m) Cement Tile/fabric Asbestos Thatched Average 1 0 0 0 0 0 0 2 0.15 36025 24111 31004 491 22908 3 0.3 50435 27156 34934 819 28336 4 0.6 56658 33883 72924 18476 45485 5 0.9 58950 92301 75106 68994 73838 6 1.2 312697 114465 98686 123714 162391

Thatched Asbestos Tile/fabric Cement Average 350000

300000

250000 200000

150000 Damage (Rs) Damage 100000 50000 0 0 0.15 0.3 0.6 0.9 1.2 Depth (m)

Figure 22: Depth damage function for different houses ASSESSING VULNERABILTY 41

AGRICULTURE DAMAGE Assumptions: 1. The damage estimation was made for paddy as it is the main agricultural produce in the study area. 2. If flood occurs in the study area, it is assumed that it remains for 3 days, as the slope is very gentle. The damage curve is estimated for this duration. Agricultural damage function varies with its various stages i.e. vegetative, reproductive, maturity and ripening stages. The ranges of yield loss of rice due to flood is given by Bureau of Agricultural Statistics of Philippines (Shrestha et al., 2014) as given in Table 26-a

Table 26-a: Range of Yield Loss of Rice Crops due to Flood

Days of Submergences

Growth stage 1-2 days 3-4 days 5-6 days 7 days

Estimated yield loss (%)

Vegetative stage 10-20 20-30 30-50 50-100

Reproductive stage (Partial 10-20 30-50 40-85 50-100 inundated)

Reproductive stage (Partial 15-30 40-70 40-85 50-100 inundated)

Maturity stage 15-30 40-70 50-90 60-100

Ripening stage 5 10-20 15-30 15-30

The rice plant has the following height at various stages as given in Figure 19b (BAS: 2013):

Table 26-b: Height of rice pant during different stages

Stage of rice Time Plant Height (cm)

Seedling/seedbed stage 20 days < 30

Newly planted stage 1-20 days after sowing 30-40

Vegetative stage 21-45 days from rice planting 40-100 in paddy field

Reproductive stage 46-75 days 100-130

Maturity stage 76-115 days 130

Ripening stage 116-130 days 130

ASSESSING VULNERABILTY 42

Information above clearly depicts that the yield loss (%) is different for different crop stages and height. Since NDRI (2012) did not take these factors into consideration, the approach taken by Shrestha et al. (2014) was used to estimate yield loss in this study. Based on above data, considering the height of the paddy plants (Table 126-b) and yield loss (Table 26-a), following depth damage curve is estimated as given in Table 27 and shown in Figure 23. The figure in the table represents the estimated yield loss (%).

Table 27: Depth damage estimate according to crop stages Depth (m) Vegetative Reproductive Maturity Ripening 0 0 0 0 0 0.25 0 0 0 0.5 20 0 0 0 0.75 20 40 0 0 1 20 45 0 0 1.25 20 50 50 10 1.5 20 50 50 10

60 Vegetative 50 Reproductive Maturity

40

Ripening (%) 30

20 Yield Loss Loss Yield

10

0 0 0.25 0.5 0.75 1 1.25 1.5 Depth (m)

Figure 23: Depth Damage Curve for Yield Loss

ROAD Flooding over the road can cause significant disruption in transportation. It may lead to access difficulties for emergency services during events and disruption to road users and the community during and aftermath of flood events. While talking about depth damage with respect to road, two types of damages therefore, needs to be considered. One is vehicle damage and the other is road damage itself.

a. Vehicle Damage

In car, damage will be experienced as soon as the water is above the level of the base of the doors (and can enter the interior) which is roughly at a height of 0.3m. When the water has entered the car, a loss of about 25% occurs due to damage to seats, floor coverings or other interior fixtures (Reese and Ramsay, ASSESSING VULNERABILTY 43

2010). The electronic circuits of the cars get affected at 0.6–0.8m of water. Beyond this height the damage increases continuously up to 1.5m where the entire car is filled with water. At this stage 100% damage is assumed to be occurred (Reese and Ramsay, 2010). It is assumed that it is applicable to Nepalese case in the study area too. The depth damage curve for vehicle is presented in Figure 24.

100

80

60

40 Vehicle DamageVehicle% 20

0 0 0.5 1 1.5 2 Depth (m)

Figure 24: The depth damage curve for car

b. Road

Damage to roads from flooding is normally relatively minor compared to other categories such as buildings and contents. The American Lifelines Alliance, 2005 (cited in Reese and Ramsay, 2010) listed the following damages, ranging from ditch scour to complete collapse of a length of road bed or embankment:

 Saturation and collapse of inundated road beds  Loss of paved surfaces through flotation or delimitation  Washout of unpaved roadbeds  Erosion and scour of drainage ditches, sometimes to the extent of undermining shoulders and roadbeds  Damage to or loss of under drain and cross-drainage pipes  Blockage of drainage ditches and under drains by debris, exacerbating erosion and scour  Undermining of shoulders when ditch capacity is exceeded  Washout of approaches to waterway crossings  Deposition of sediments on roadbeds The damage values vary depending on the type of road. However, in Kok (2001) report, there is a damage function for roads and railroads, with 5m as a maximum Genovese (2006). The damage function proposed by Kok is assumed valid for the study area and is given in Table 28. Linear interpolation can be used between these points (Genovese, 2006).

ASSESSING VULNERABILTY 44

Table 28: Damage function for road Inundation depth (m) Damage % 0 0 5 100

INDUSTRIAL AREA

The study area is one of the biggest industrial areas of Nepal. Depth damage curve for such area is constructed based on Kok (2001) figures as recommended by Genovese (2006), assuming that it is equally valid for our case too. It is given in Table 27 and Figure 26. Intermediate value can be estimated by linear interpolation (Genovese, 2006).

Table 29: Depth Damage for Industrial Area

Depth (m) Damage % 0 0 1 40 2 80 3 90 4 100

100

80

60

40 Damage % Damage

20

0 0 1 2 3 4 5 Depth (m)

Figure 25: Depth Damage for Industrial Area

ASSESSING VULNERABILTY 45

REFRENCES

Chakraborty, J., Tobin, G. A. and Montz, B. E., 2005. Population Evacuation: Assessing Spatial Variability in Geo- Physical Risk and Social Vulnerability to Natural Hazards. Natural Hazards Review, American Society of Civil Engineers, Vol. 6, No. 1, pp. 2-33. DWIDP, 2012. Disaster Review 2011. Department of Water Induced Disaster Prevention. Government of Nepal. DWIDP, 2013. Disaster Review 2012. Department of Water Induced Disaster Prevention. Government of Nepal. DWIDP, 2014. Disaster Review 2013. Department of Water Induced Disaster Prevention. Government of Nepal. Genovese , E., 2006. A methodological approach to land use-based flood damage assessment in urban areas: Prague case study, European Union. Herath, S., 2003. Flood Damage Estimation of an Urban Catchment Using Remote Sensing and GIS. International Training Program on Total Disaster Risk Management. Hebb, A. and Mortsch, L., 2007. Floods: Mapping Vulnerability in the Upper Thames Watershed under a Changing Climate. Project report XI, University of Waterloo, pp 1-53. ICIMOD , 2008. Koshi Flood Disaster. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal Kok M., 2001. Damage functions for the Meuse River floodplain, Internal report, JRC (Ispra). NCVST, 2009. Vulnerability through the Eyes of Vulnerables: Climate Change Induced Uncertanities and Nepal's Development Predicaments. Nepal Climate Vulnerability Study Team. NPC, 2012. Impact Evaluation of Susari-Morang Irrigation Project. National Planning Commission Secretariat M & E Division, SMES 2. NDRI, 2013, Disaster Risk Reduction and Climate Change Adaptation in Koshi River Basin, Nepal, Nepal Development Research Institute, Lalitpur, Nepal. Pelling, M., 2003. The Vulnerability of Cities: Natural Disaster and Social Resilience. Earth Scan, London. Reese, S. and Ramsay, S., 2010. RiskScape: Flood fragility methodology. New Zealand Climate Change Research Institute Victoria University of Wellington, Wellington, New Zealand. Shrestha, B.B.; Okazumi, T. ; Miyamoto, M. and Sawano, H., 2014, Development of Flood Risk Assessment Method for data-poor river basins: a case study in the pampanga river basin, Philippines UNDRO, 1991. Mitigating Natural Disasters: Phenomena, Effects and Options. United Nations, New York, 164-pp. UNDP, 2013. Gender and Climate Change, Training Module 3, Disaster Risk Reduction. United Nation and Development Programme. Wallingford, H. R., 2006. Flood Hazard Research Centre, Middlesex University, Risk & Policy Analysts Ltd. The Flood Risks to People Methodology

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ANNEX

Annex 1: Yield in Mt per Ha for five major staple crops in Morang and Sunsari district

Crops Paddy Wheat Maize Millet Barley Year Morang Sunsari Morang Sunsari Morang Sunsari Morang Sunsari Morang Sunsari 1976/77 1.71 1.75 1.00 0.95 1.50 1.42 1.08 0.85 0.79 0.85 1977/78 1.81 2.01 1.14 1.10 1.50 1.42 0.94 1.12 0.80 0.86 1978/79 1.65 1.65 1.16 1.20 1.50 1.42 0.95 1.12 0.80 0.77 1979/80 1.78 1.82 1.20 1.20 1.50 1.28 0.86 0.98 1.00 1.00 1980/81 1.80 1.82 1.35 1.35 1.59 1.59 0.95 0.98 0.90 0.80 1981/82 1.90 2.30 1.84 1.90 1.60 1.60 0.71 0.90 0.80 0.80 1982/83 1.60 1.80 1.51 1.50 1.70 1.68 1.20 0.90 0.88 1.00 1983/84 1.91 2.32 1.60 1.60 1.78 1.76 0.90 0.90 0.88 1.00 1984/85 1.87 2.19 1.36 1.41 1.73 1.89 1.00 1.00 0.80 0.80 1985/86 2.06 2.32 1.50 1.48 1.50 1.50 0.95 1.00 0.88 0.80 1986/87 1.90 2.10 1.60 1.50 1.42 1.59 0.90 1.08 0.89 0.80 1987/88 2.12 2.09 1.63 1.53 1.76 2.10 0.80 1.18 0.89 0.75 1988/89 2.38 2.32 1.71 1.62 1.50 1.66 1.01 1.97 1.00 1.00 1989/90 2.44 2.35 1.70 1.62 1.63 1.76 1.08 1.50 0.90 1.00 1990/91 2.38 2.48 1.65 1.61 1.72 1.67 1.09 0.66 1.00 11.00 1991/92 2.43 2.58 1.63 1.59 2.09 1.75 1.09 0.54 1.00 1.00 1992/93 2.30 2.44 1.45 1.50 2.10 1.95 1.00 0.60 1.00 1.00 1993/94 2.51 2.50 1.58 1.68 1.70 1.94 1.00 0.80 0.86 1.00 1994/95 2.20 2.17 1.71 1.64 1.72 1.94 1.02 0.61 0.86 1.00 1995/96 2.60 2.67 1.70 1.72 1.80 1.75 0.91 0.56 1.00 1.00 1996/97 2.65 2.81 1.72 1.74 1.82 1.73 0.86 1.10 1.00 6.00 1997/98 2.63 2.79 1.65 1.69 1.85 1.76 0.91 1.10 1998/99 2.59 2.73 1.88 1.74 1.89 1.80 0.90 1.36 1999/00 2.80 2.93 1.90 2.30 1.48 2.06 1.10 1.13 2000/01 3.10 2.99 1.99 2.00 1.80 1.90 1.12 1.15 2001/02 3.10 2.73 2.15 2.20 1.58 2.50 1.10 1.00 2002/03 3.11 3.17 2.30 2.51 1.81 1.95 1.15 1.50 2003/04 3.28 3.40 2.15 2.50 1.81 1.95 1.15 1.50 2004/05 3.28 3.12 2.26 2.42 2.00 1.95 1.08 1.00 2005/06 3.12 3.10 2.20 2.32 2.25 1.95 1.25 1.27 2006/07 2.98 3.00 2.30 2.50 2.10 2.50 1.01 0.90 2007/08 3.10 3.10 2.40 2.40 2.26 2.53 1.19 1.00 2008/09 3.17 2.76 2.20 2.62 2.32 2.04 1.20 1.00 2009/10 3.17 2.68 2.27 2.61 2.19 2.61 1.20 1.01 2010/11 3.30 2.90 2.36 2.50 2.81 2.74 1.20 1.00 2011/12 3.55 3.10 2.40 2.60 3.00 2.80 1.20 0.93 2012/13 3.13 3.06 2.28 2.81 3.30 3.44 1.20 0.91

Annex 2: National Annual Average Retail Price of Rice per Kilograms (CBS) ASSESSING VULNERABILTY 47

1999/ 2000/ 2001/ 2002/ 2003/ 2004/ 2005/ 2006/ 2007/ 2008/ 2009/ 2010/ 2011/ Rice type 00 01 02 03 04 05 06 07 08 09 10 11 12 Av. Rice Coarse 20.51 17.91 17.07 17.2 17.88 18.13 29.06 22.63 25.51 29.96 31.58 34.93 34.45 24.37 Raw Rice Medium 24.07 22.23 21.03 21.78 23.48 22.94 27.08 28.18 30.99 36.01 40.95 43.72 43.4 29.68 Raw Rice Fine 34.17 34.54 33.56 32.93 36.02 34.36 40.35 43.83 45.85 52.5 61.06 65.28 63.72 44.47 Beaten Rice 27.23 24.13 23.97 22.53 23.99 23.11 26.86 27.15 30.34 37.98 40.97 46.81 47.86 30.99 Annex 3: Retail Price of Rice per Kilograms in Biratnagar Market Based on DADO 2012/2013

Rice type Sharwan Bhadra Asoj Kartik Mangsir Poush Magh Falgun Chaitra Baisakh Jestha Ashad Av. Rice Coarse 28 30 33 33 28 30 87 28 31 31 31.3 30.66 35.1 Raw Rice Medium 35.6 41 41 42 35 40 31 34 36 37.6 38.3 41.66 37.8 Raw Rice Fine 53 53.3 53.3 53.3 56 56 45 53.3 70.6 71 70 71.66 58.9 Beaten Rice 49 48.3 48.3 48.3 46 45 36 35 40 39.3 38.6 41 42.9 Annex 4: Wholesale Price of Paddy per Kilograms in Briatnagar Market Based on DADO 2013/2014

Type Sharwan Bhadra Asoj Kartik Mangsir Poush Magh Falgun Chaitra Baisakh Jestha Ashad Av Paddy Coarse 19.3 19.3 18 13.33 12.5 13.33 14.3 13.1 15.08 15.08 15.08 15.25 15.30 Paddy Medium 22.5 22.5 22 16.17 13.5 15.33 15.6 14.1 16.08 16.25 16.25 16.25 17.21 Paddy Fine 26.8 26.8 27 27 24.3 30 29.3 29.3 32.6 33 31.66 32 29.15 Annex 5: Cropping pattern in half irrigated and non-irrigated lands of Saptari district (Based on DADO report of Spatari, 2067/2068) Half irrigated Non-irrigated Paddy-Wheat-Barren Paddy-Onion-Barren Paddy-Oil seeds- Barren Paddy-Barren-Barren Paddy-Potato-Barren Paddy-Pulses-Barren Paddy-Barren-Barren Paddy-Potato-Barren Paddy-Onions-Barren Paddy-Vegetables-Barren Paddy-Pulses-Barren Maize-Oilseeds-Barren Paddy-Vegetables-Barren Paddy-Wheat-Pulses

Annex 6: Average cost, return and net profit of major agricultural commodities, 2011/2012 (*Source: MOAD, 2013) Value of main product Gross income Net profit Input Output Ratio Land Yield Total Farm Farm Farm S. Farm Gate Market Gate Market Cost Gate Market Gate Market N. Belt Crops Type Kg.Ha. Cost Rs./Ha Rs./Ha Rs./Ha Rs./Ha Rs./Qt. Rs./Ha Rs./Ha Ratio/Ha Ratio/Ha 1 Terai Paddy Chaite Irrig. 4065 48847 62397 62560 66820 79882 1092 17973 31035 1.368 1.635 2 Terai Paddy Irrig. 4083 48669 62674 62837 66942 80456 1087 18273 31787 1.375 1.653 3 Terai Paddy Unirri. 3868 40785 57172 57800 61257 77181 950 20472 36396 1.502 1.892 4 Hill Paddy Unirri. 3986 49865 59989 60094 64468 74114 1138 14603 24249 1.293 1.486 5 Mt Wheat Irri. 2812 75256 144593 152643 148175 217230 2548 72919 141974 1.969 2.887 6 Hill Wheat Unirri. 3017 42377 51439 51877 54043 62804 1318 11666 20427 1.275 1.482 7 Hill Wheat Irrig. 3237 48664 56485 56275 59196 66693 1419 10532 18029 1.216 1.37 8 Terai Wheat Unirri. 3012 44895 54366 54109 57306 66476 1392 12411 21581 1.276 1.481 9 Terai Wheat Irrig. 3351 49051 59714 58511 62898 73226 1368 13847 24175 1.282 1.493 10 Hill Maize Unirri. 3184 43643 48428 48699 51863 5633 1262 8220 -38010 1.188 0.129 11 Hill Maize Irrig. 3276 41588 497119 493660 50216 62409 1196 8628 20821 1.207 1.501 12 Terai Maize Irrig. 3516 43283 57170 57284 61047 74582 1120 17764 31299 1.41 1.723 13 Terai Maize Unirri. 3238 38330 48408 48848 50951 60706 1105 12621 22376 1.329 1.584 14 Terai Sugarcane Irri. 134300 100533 552644 552644 75 452111 75 452111 5.497 15 Terai Cotton Irri. 2580 58415 159960 101544 2264 43129 2264 43129 1.738 * MOAD, 2013. Statistical Information on Nepalese Agriculture, 2012/2013. Ministry of Agricultural Development, Singha Durbar, Kathmandu, Nepal.

Annex 7: Area, production and yield of paddy in Morang, Sunsari and Saptari district Estimated Area under production Total Production (M.ton) Yield (Mt/ha) Year (ha) Morang Sunsari Saptari Morang Sunsari Saptari Morang Sunsari Saptari 1976/77 83453 46281 70200 142704 80991 118080 1.71 1.75 1.68 1977/78 87600 46330 73460 158870 93131 114900 1.81 2.01 1.56 1978/79 78430 50350 72850 129410 83080 131130 1.65 1.65 1.80 1979/80 78430 50350 72850 139760 91390 144240 1.78 1.82 1.98 1980/81 78400 50340 73460 141100 91500 145450 1.80 1.82 1.98 1981/82 80470 46680 74340 152890 107360 104080 1.90 2.30 1.40 1982/83 74570 42670 69310 119180 76820 76900 1.60 1.80 1.11 1983/84 81670 45490 72810 156100 105490 127720 1.91 2.32 1.75 1984/85 80000 50230 72030 149980 109850 115940 1.87 2.19 1.61 1985/86 74830 48700 73540 154150 112980 2.06 2.32 1986/87 73430 45200 65530 139620 95110 86130 1.90 2.10 1.31 1987/88 80070 54360 68820 169440 113850 134120 2.12 2.09 1.95 1988/89 80400 56380 68100 191600 130540 116370 2.38 2.32 1.71 1989/90 83000 56860 65200 202850 133620 130390 2.44 2.35 2.00 1990/91 84400 57600 66300 200980 142600 146200 2.38 2.48 2.21 1991/92 80180 51600 65640 194890 133210 158340 2.43 2.58 2.41 1992/93 70150 43860 34130 161350 107020 75080 2.30 2.44 2.20 1993/94 82200 55900 72410 206320 139780 172500 2.51 2.50 2.38 1994/95 79200 54960 80000 174250 119100 145450 2.20 2.17 1.82 1995/96 91400 58500 69150 237380 156140 167240 2.60 2.67 2.42 1996/97 91420 58500 69900 242040 164100 174800 2.65 2.81 2.50 1997/98 91420 58500 69900 240620 163140 173780 2.63 2.79 2.49 1998/99 92800 58600 70050 240280 159980 169100 2.59 2.73 2.41 1999/00 95050 61775 71200 265887 180787 192810 2.80 2.93 2.71 2000/01 95000 62000 69190 294500 185380 165650 3.10 2.99 2.39 2001/02 85000 47850 70190 263741 130625 168855 3.10 2.73 2.41 2002/03 94789 61715 70000 294435 195822 196589 3.11 3.17 2.81 2003/04 94790 61130 70000 311250 191815 196589 3.28 3.14 2.81 2004/05 98000 61116 67000 321832 190807 201000 3.28 3.12 3.00 2005/06 98070 61140 68000 305740 189530 157500 3.12 3.10 2.32 2006/07 81360 54942 52000 242476 164826 95680 2.98 3.00 1.84 2007/08 88290 53600 68000 273699 166160 153000 3.10 3.10 2.25 2008/09 88200 51158 68400 279912 141360 173500 3.17 2.76 2.54 2009/10 77120 49991 49903 244500 133950 150724 3.17 2.68 3.02 2010/11 78200 53791 37691 258060 155994 116842 3.30 2.90 3.10 2011/12 78200 53550 60000 277610 166000 177000 3.55 3.10 2.95 2012/13 82840 52550 30219 259289 160650 60388 3.13 3.06 2.00