A Final Report on:

GIS BASED FLOOD HAZARD MAPPING AND VULNERABILITY ASSESSMENT OF PEOPLE DUE TO CLIMATE CHANGE: A CASE STUDY FROM KANKAI

WATERSHED, EAST NEPAL

Submitted to: National Adaptation Programme of Action (NAPA)

Ministry of Environment

Submitted by: Principal Researcher : Shantosh Karki (skipsantosh@gmail.com)

Member: Arbindra Shrestha, Mukesh Bhattarai, Sunil Thapa Mentor: Prof. Dr. Madan Koirala

GIS Supervisor: Mr. Ananta Man Singh Pradhan January, 2011

Supported by:

i

Executive summary:

Flooding is a serious, common, and costly hazard that many countries face regularly. Flooding

due to excessive rainfall in a short period of time is a frequent hazard in the flood plains of

Nepal during monsoon. In this study, we have assessed flood hazards, their impacts, and the

resilience of communities at the watershed level. Floods occur repeatedly in Nepal and cause

tremendous losses in terms of property and life, particularly in the lowland areas of the

country. Hence, they constitute a major hazard. The main objective of this study is to integrate

flood simulation model, remotely sensed data with topographic and socio-economic data in a

GIS environment for flood risk mapping in the flood plain of Kankai River in Nepal. Identification

and mapping of flood prone areas are valuable for risk reduction. The results obtained from GIS

modeling were then verified with social approach through vulnerability assessment. Flood

danger level and warning level was identified by using maximum instantaneous discharge data

and gauge height. Water level at 3.7m is marked as warning level and at 4.2m is marked as

danger level. The flood frequency analysis was done through three different methods and

finally Gumbel distribution was adopted for the hazard mapping. Here a 25 year-return period

flood hazard map and a 50 year-return period flood hazard map were prepared. The potential

damage of the study area was calculated. Total 59.3 sq. km and 59.8 sq.km of the study area

will be under flooding in a 25 year- return period flood and 50 year-return period flood

respectively. The result shows that agriculture system of the study area is in more vulnerable

position. The hazard prone area will be considerably increased from 25 year-return period flood

to 50 year-return period flood. Level of hazard shows that high hazard area will be increased

and more settlement will be under the high hazard zone. Vulnerability assessment regarding

flooding and climate change depicted that peoples' livelihood are worsening each year. Special

response mechanism and appropriate mitigation measure is needed to solve climate change

induced disaster. This hazard zone should be viewed with special attention and given utmost

importance. The results can thus be utilized as baseline information for the policy making to

combat against climate induced disaster.

Keywords: floods, rainfall, hazard, monsoon, GIS (Geographic Information System), modeling,

vulnerability, climate, disaster

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Contents Executive summary: ........................................................................................................................... i

Contents ............................................................................................................................................ ii

List of Figure: .................................................................................................................................... iii

List of Table: ..................................................................................................................................... iii

Acronyms and Abbreviations ............................................................................................................. iv

1. INTRODUCTION ..............................................................................................................................1

1.1 Background ................................................................................................................................... 1

1.2 Objective of the study .................................................................................................................... 2

1.3 Study area ..................................................................................................................................... 2

1.3.1 General Information ................................................................................................................ 2

1.3.2 Climate ................................................................................................................................... 3

1.3.2 Hydro meteorology ................................................................................................................. 3

1.3.4 Characteristics of Most Recent Floods ..................................................................................... 4

2. METHODS ......................................................................................................................................4

2.1 Source of Data ............................................................................................................................... 5

2.2 Data Collection .............................................................................................................................. 5

2.3 Verification of Flood Mapping in the Field ...................................................................................... 5

2.4 Preparation of Triangulated Irregular Network (TIN) ...................................................................... 5

2.5 Model Application ......................................................................................................................... 5

2.6 Flood Frequency Analysis ............................................................................................................... 6

2.7 Vulnerability Assessment ............................................................................................................... 6

2.8 Data Analysis ................................................................................................................................. 6

3. RESULT...........................................................................................................................................7

3.1 Hydro Meteorological Analysis ..................................................................................................... 7

3.2 Discharge analysis: ......................................................................................................................... 9

3.3 Flood Frequency Analysis ............................................................................................................... 9

3.4 TIN of Kanaki Watershed.............................................................................................................. 10

3.5 Preparation of Landuse Map ........................................................................................................ 10

3.6 Flood Hazard Analysis .................................................................................................................. 11

3.7 Flood Vulnerability Analysis ......................................................................................................... 12

3.8 Vulnerability Assessment of the Study Area by VDC Level ............................................................ 13

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3.9.1 Demography: ........................................................................................................................ 14

3.9.2 Economic Condition of the affected population: .................................................................... 15

3.10 Impact of Flooding and Climate Change ..................................................................................... 15

3.10.1 Impacts of Floods ............................................................................................................... 15

4. DISCUSSION ................................................................................................................................. 18

5. CONCLUSION................................................................................................................................ 19

6. RECOMMENDATION: .................................................................................................................... 20

7. ACKNOWLEDGEMENTS ................................................................................................................. 20

REFERENCES .................................................................................................................................... 20

Annex-Section ................................................................................................................................. 23

List of Figure: Figure 1: Map showing Study area3

Figure 2: Average monthly rainfall at Gaida meteorological station for 1972-2005..3

Figure 3: Average monthly discharge at Mainachuli (Source: DHM, 2010)..4

Figure 4: Annual Rainfall versus Recurrence Interval for prediction of Maximum

Rainfall at particular Recurrence interval....7

Figure 5: Annual Rainfall versus Recurrence Interval for prediction of Minimum

Rainfall at particular Recurrence Interval..8

Figure 6: Annual instantaneous maximum flood gauge height with warning and danger

level.9

Figure 7: TIN of the study area...10

Figure 8: Landuse map of the study area.10

Figure 9: Flood Hazard map of the study area for 25 year return period flood . 11

Figure 10: Flood Hazard map of the study area for 50 year return period flood.11

Figure 11: Return Period-Flood Depth Relationship 12

Figure 12: Flood Depth-Area Relationship..12

Figure 13: Impact of flooding and climate change15

List of Table:

Table 1: Prediction for the maximum rainfall at different recurrence interval7

Table 2: Prediction for the minimum rainfall at different recurrence interval..8

Table 3: Flood frequency analysis for various return period.10

Table 4: Landuse pattern of the study area10

Table 5: Calculation of Flood Area according to Flood Hazard.12

Table 6: Vulnerable areas for 25 years and 50 years flooding..13

Table 7: VDC wise flood hazard mapping..14

Table 8: Rice Sufficiency by Wealth Ranking 16

Table 9: Vulnerability Score..18

iv

Acronyms and Abbreviations

cumecs Cubic meter per second

DEM Digital Elevation Model

DHM Department of Hydrology and Meteorology

DWIDP Department of Water-Induced Disaster Prevention

GIS Geographic Information System

GPS Global Positioning System

HEC-RAS Hydraulic Engineering Centers River Analysis System

Hrs/hrs Hours

km Kilometer

km2 Square kilometer

m Meter

m3 Cubic meter

MoHA Ministry of Home Affairs

sq km Square kilometer

TIN Triangulated Irregular Network

VDC Village Development Committee

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1. INTRODUCTION

1.1 Background

Floods are the most common natural disasters that affect societies around the world. Dilley et

al. (2005) estimated that more than one-third of the worlds land area is flood prone affecting

some 82 percent of the worlds population. About 196 million people in more than 90 countries

are exposed to catastrophic flooding, and that some 170,000 deaths were associated with

floods worldwide between 1980 and 2000 UNDP (2004). These figures show that flooding is a

major concern in many regions of the world. Globally, the economic cost of extreme weather

events and flood catastrophes is severe, and if it rises owing to climate change, it will hit

poorest nations the hardest consequently, the poorest section of people will bear the brunt of

it. The number of major flood disasters in the world has risen relentlessly over recent time.

There were six in the 1950s; seven in the 1960s; eight in 1970s; eighteen in the 1980s; and

twenty six in the 1990s (UNDP, 2004).

Nepal, the central part of the Hindu-Kush Himalayan, has more than 6,000 rivers and rivulets.

Floods and landslides, which are triggered by heavy precipitation, cause 29% of the total annual

deaths and 43% of the total loss of properties in Nepal (DWIDP, 2004).The Terai amounting to

only 17% of the total area of the country is regarded as the granary of Nepal and the problem

of flooding in this region is of utmost concern. In recent years, between 1981 and 1998, three

events of extreme precipitation with extensive damage have been reported (Chalise and Khanal

2002). Floodplain analysis and flood risk assessment of the Babai Khola, using GIS and numerical

tools (HEC-RAS & AV-RAS) was carried out by Shrestha (2000). Similarly, GIS was applied for

flood risk zoning in the Khando Khola in eastern Terai of Nepal by Sharma et. al. (2003). Awal et.

al. (2003, 2005 and 2007) used hydraulic model and GIS for floodplain analysis and risk mapping

of Lakhandei River. After the disastrous climatologic event of 1993, hazard maps were prepared

for the severely affected areas of Central Nepal, (Miyajima and Thapa, 1995).

In the context of global warming, the probability of potentially damaging floods occurring is

likely to increase as a consequence of the increase in the intensity of extreme precipitation

events (i.e. >100 mm/day)(Baidya et al. 2007) and the status of glacial lakes in high mountain

areas, Global Circulation Model projects a wide range of precipitation changes, especially in the

monsoon: 14 to +40% by the 2030s increasing 52 to 135% by the 2090s (New et.al 2009). The

monsoon precipitation pattern is changing too; with fewer days of rain and more high-intensity

and incessant rainfall events.

Various definitions of vulnerability have been provided in the context of natural hazards and

climate change (Varnes 1984; Blaikie et al. 1994; Twigg 1998; Kumar 1999; Kasperson 2001).

From these definitions, vulnerability can be viewed from the perspective of the physical, spatial

or locational, and socioeconomic characteristics of a region. In recent years, a number of

studies have recognized the importance of estimating peoples vulnerability to natural hazards,

rather than retaining a narrow focus on the physical processes of the hazard itself (Hewitt,

1997; Varley, 1994; Mitchell, 1999). Cannon (2000) argued that natural disaster is a function of

both natural hazard and vulnerable people. He emphasized the need to understand the

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interaction between hazard and peoples vulnerability. Nepals vulnerability to climate-related

disasters is likely to be exacerbated by the increase in the intensity and frequency of weather

hazards induced by anthropogenic climate change (IPCC, 2007). Vulnerability to flood hazards is

likely to increase unless effective flood mitigation and management activities are implemented.

An important prerequisite for developing management strategies for the mitigation of extreme

flood events is to identify areas of potentially high risk to such events, thus accurate

information on the extent of floods is essential for flood monitoring, and relief (Smith, 1997).

The main objective of this study is to integrate flood simulation model and remotely sensed

data with topographic and socio-economic data in a GIS environment for flood risk mapping in

the flood plain of Kankai River in the Eastern Nepal. Flooding is a serious, common, and costly

hazard that many countries face regularly. Identification and mapping of flood prone areas are

valuable for risk reduction. Flood risk mapping consists of modeling the complex interaction of

river flow hydraulics with the topographical and land use characteristics of the floodplains.

Integrating hydraulic models with geographic information systems (GIS) technology is

particularly effective.

Flood-hazard risk and vulnerability mapping using GIS and RS with a geomorphic approach

In the context of flood hazard management, GIS can be used to create interactive map overlays,

which clearly and quickly illustrate which areas of a community are in danger of flooding. Such

maps can then be used to coordinate mitigation efforts before an event and recovery after

(Noah Raford, 1999 as cited in Awal, 2003). GIS, thus, provides a powerful and versatile tool to

facilitate a fast and transparent decision-making. In addition, a digital elevation model (DEM) is

prepared from the contour and spot heights and used for preparing a slope map, cross-section

profile of the terrain, and river profile; and delineating potential sites for river-bank cutting,

flood-prone areas, assuming a dam of a certain height at a given river reach. A DEM is also used

to calculate the flooded area by incorporating it into the U.S Army Corps Hydrological

Engineering River System Analysis (HEC-RAS) flood modeling software.

1.2 Objective of the study

The general objective of this study is to prepare flood-hazard map and vulnerability assessment

and to identify an appropriate mitigation activities for the Kankai Watershed. The specific

objectives of the study are:

to prepare flood hazard map of Kankai watershed using GIS .

to assess the vulnerability of people living in the floodplain area due to climate change.

to prioritize and measure the impacts of climate change through a participatory

approach and statistical tools.

1.3 Study area

1.3.1 General Information

The study was conducted on Kankai Watershed. The Kankai River is one of the class II type

rainfed perennial rivers of eastern Nepal. It originates in Mahabharat range at Chamaita Village

Development Committee (VDC) in Ilam district. At the place of its origin it is called Deomai

3

Khola. The catchment area of the study basin is 1284km2. The altitude of its origin is about

1820m. The altitude in Sukedangi near the district border of Ilam and Jhapa is about 120m, and

about 70 m in the Indo-Nepal border. 48 VDCs lie inside the watershed covering the Ilam and

Jhapa districts. 40 VDCs of Ilam and 8 VDCs of Jhapa). Total population of the watershed is

322951 (CBS 2001).

Figure 1: Map showing Study area

1.3.2 Climate

Kankai river basin lies in the south-eastern part of the country. It has tropical and subtropical

climate regime. The period from March to June is predominantly hot and dry, July to August is

hot and humid, September to October is pleasant, and November to February is cool and dry.

The hot wave during the summer and cold wave during the winter reflects harshness of the

climate in the study area. The temperature ranges up to 460C in the summer to 2

0 during

winter. The mean daily temperature at Gaida Meteorological station (25.580 N, 87.90

0 E,

elevation 143m) has been estimated as 24.50C. Similarly, the relative humidity (RH) is 75% and

vapour pressure is 23.79. Kankai watershed falls in the class of 75-85% annual average RH. The

estimated mean daily vapour pressure is 20. The average sunshine hour for almost 8 months of

a year is about 80%, and falls below 50% during monsoon.

1.3.2 Hydro meteorology

The annual mean precipitation in the study area ranges from 2000 to 3000mm. The average

annual precipitation at Gaida station, Damak station, Sanischare station and Ilam Tea state

stations are 2734 mm, 2369 mm, 2794 mm, 1574 mm respectively.

Figure 2: Average monthly rainfall at Gaida meteorological station for 1972-2005

China

India

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Figure 3: Average monthly discharge at Mainachuli (Source: DHM, 2010)

The mainachuli gauging site (Station no. 795) has stream flow record from 1972 to date. The

average monthly variation of flow at this station is shown in figure 3. The annual average

discharge at this station is 58.9m3/s (based on 1972-2006 data). The discharge increases rapidly

from May and reaches maximum in July.

1.3.4 Characteristics of Most Recent Floods

The increased frequency of flooding in recent times together with disturbed institutional set up

in the region due to domestic political conflict has severely affected people in the region. The

marginalized group having less education, fatalistic attitude and lacking development

infrastructure have been most affected by the flood disaster.

2009 Flood:

Seven VDCs were flooded by swollen Kankai and Biring rivers in southern Jhapa. More than 500

houses were submerged and 100s of bighas of paddy field were inundated. Hundereds of

people from Topgachhi, panchgacchi, Tanghadubba, Rajgadh, Dangibari and Sarnamati fled

their villages to safer places after the floods entered the VDCs. While the flood in Kankai caused

much damage in Topgachhi 8 & 9, Rajgadh VDC also suffered badly. Even if no loss of life were

reported, the villagers incurred heavy loss of property as hundreds of bighas of land have been

turned into riverbed. The worst affect were those people who had paddy fields near the river

banks, i.e. on the flood plain.

2. METHODS

The proposed method of this research involved several steps: (1) Rainfall data, stream flow

data, topographic data as well as geological and land used data were collected, stored and

preprocessed. (2) Flood frequency analysis was done using the Gumbels Method. Gumbel

method is most widely used probability distribution functions for extreme values in hydrological

and meterological studies for predication of flood peaks, maximum rainfall etc. This method is

used in this project to calculate peak discharge. (3) The general method adopted for floodplain

analysis and flood risk assessment in this study basically consists of five steps: (a) Preparation of

TIN in ArcView GIS 3.3 (b) Preparation of land cover map (c) GeoRAS Pre-processing to generate

HEC-RAS Import file (d) Running of HEC-RAS to calculate water surface profiles (e) Post-

processing of HEC-RAS results and floodplain mapping. (4) Vulnerability assessment

Average monthly Discharge

Dis

cha

rge

(cu

me

cs)

5

2.1 Source of Data

The information needed for flood hazard and vulnerability mapping was obtained from three

different sources: i) maps, aerial photographs, and imagery; ii) field survey and group

discussions; and iii) published and unpublished documents.

2.2 Data Collection

The information needed for flood hazard and vulnerability mapping was obtained from three

different sources: i) maps, aerial photographs, and imagery; ii) field survey and group

discussions; and iii) published and unpublished documents. Meteorological data, particularly

rainfall and temperature of the nearest stations Gaida Station, and similarly, the hydrological

data of Kankai River; Mainachuli Station were collected from the Department of Hydrology and

Meteorology. The walkover survey was conducted for gathering information on river

undercutting, debris flow, and flooding in the watershed.

2.3 Verification of Flood Mapping in the Field

For the preparation of a hazard map, field mapping was conducted. During the field study,

detailed information regarding old river courses, old flood marks, bank height, river

undercutting, channel shifting, and effect of flooding on civil structures, was collected. Local

people were interviewed to get information on the history of river flooding, observed flood

levels, socioeconomic impact of floods, and hazard assessment perception of the community.

2.4 Preparation of Triangulated Irregular Network (TIN)

The contour data provided by the Department of Survey, cross section data & spot height data

was used for Triangulated Irregular Network generation. ArcView 3.3 was used to generate TIN

which was used as Digital Elevation Model (DEM) required in GeoRAS environment in order to

prepare data sets required as input to the HEC-RAS simulation.

2.5 Model Application

PreRAS, postRAS and GeoRAS menus of HEC-GeoRAS extension in ArcView GIS environment

were used for creating required data sets, making import file for model simulation in HEC-RAS.

(a) Pre GeoRAS application: The preRAS menu option was used for creating required data sets

for creating import file to HEC-RAS. Stream centerline, main channel banks (left and right), flow

paths, and cross sections were created. 3D layer of stream centerline and cross section was also

created. Thus, after creating and editing required themes, RAS GIS import file was created. (b)

HEC RAS application: This is the major part of the model where simulation is done. The import

file created by HEC-GeoRAS was imported in Geometric Data Editor interface within HEC-RAS.

All the required modification, editing was done at this stage. The flood discharge for different

return periods were entered in steady flow data. Reach boundary conditions were also entered

in this window. Then, water surface profiles were calculated in steady flow analysis window.

After finishing simulation, RAS GIS export file was created. Water surface profiles were

computed from one cross section to the next by solving the energy equation. The flow data

were entered in the steady flow data editor for two return periods as 25 year and 50 year.

Boundary condition was defined as critical depth for both upstream and downstream. Sub

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critical analysis was done in steady flow analysis. Then after, water surface profiles were

computed. The resulted was exported creating the RAS GIS export file.

2.6 Flood Frequency Analysis

Although a number of methods exist for the determination of maximum flood magnitudes, the

historical hydro-meteorological data recorded during the past events was more reliable for the

estimation of maximum probable flood. The monthly rainfall data recorded in the catchment of

the given and the adjacent basins were correlated with the respective hydrological records.

Based on the analysis, the relation between the rainfall and runoff was established.

Different three methods were applied for the flood frequency analysis Modified Dicken's

formula, Regional analysis by WECS method and Gumbel distribution. Out of them Gumbel's

method was selected because it gave largest discharge comparing to others. The Gumbels

method is the most widely used probability distribution function for extreme values in

hydrologic and meteorological studies for prediction of flood peaks and maximum rainfalls

(Subramanya, 1994).

2.7 Vulnerability Assessment

Vulnerability assessment of the study area was carried out by two different methods (a) GIS

based flood vulnerability assessment, where 25 year return period and 50 year return period

flood were calculated through Gumbel distribution. After that with the help of HEC-GeoRAS,

HEC-RAS, ARCView GIS 3.3, ArcGIS 9.3, a flood map was produced and finally vulnerable areas

were identified for 25 years flood and 50 years flood as mentioned in results; (b) another

method applied to analyze past impact by the changing climate, perception of people on

climate change and adaptation practice was carried out through Participatory Vulnerability

Approach (PVA) tools where household questionnaire survey, interview with key informants

were done within the study area. PVA is considered to be an effective tool for climate change

impact studies and for developing adaptation strategies. The future impact will also be

stimulated through the peoples perception and their coping capacity. Walkover survey

(transact walk), time line, cause and effect, problem tree, social mapping, focus group

discussion, key informant survey were used during the field survey. And finally by rating the

entire variables, final vulnerability scores were calculated using a manual published by

Livelihoods & Forestry Programme (2010). The formula used for calculating vulnerability score

is as follows:

Vulnerability Score = (Frequency + Area of Impact)*Magnitude

2.8 Data Analysis

Flood frequency analysis of 2, 10, 25, 50, 100 and 200-Years Return Period was calculated by

Gumbels, and Modified Dickens Formula. This program intended to assist in frequency analysis

of rainfall or discharge data. The procedures used are based on Gumbel's distribution. Similarly,

floods of different return periods were calculated using WECS/DHM method. Software ArcGIS

9.3 and ArcView 3.3 were used for analysis and interpretation of GIS data. All the statistical data

was entered and analyzed using Microsoft Excel 2007. For socio-economic data excel and SPSS

software were used to analyze.

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3. RESULT

3.1 Hydro Meteorological Analysis

Precipitation

The average annual runoff of the Kankai River is 1400 x 106 m3. The average annual sediment

load of the Kankai River basin up to dam site has been estimated with 2663 m3/km2.

Analysis

The rainfall data collected from rain gauge stations are analysed for determining different

hydrological relationship. Rainfall data analysis provides an estimate of future rainfall trend that

determines the rainfall intensity and occurrence of flood producing storms. Seasonal and

annual rainfall variation can be determined.

Maximum and Minimum Rainfall

Prediction of maximum and minimum rainfall magnitude within a specified period is done by

using the Weibulls, California or Hazens formula. Usually, Hazens formula is chosen for the

computation of maximum and minimum rainfall at any desired recurrence interval because it

gives two times greater value of recurrence interval that can be obtained by using Weibulls

formula or California formula.

Maximum Rainfall

The maximum rainfall depth on any specified location can be predicted from recurrence

interval and probability of occurrence and frequency curves. The curve for the annual rainfall

versus recurrence interval is well illustrated in Figure 4.

Figure 4: Annual Rainfall versus Recurrence Interval for prediction of Maximum Rainfall at

particular Recurrence Interval

From this curve, the prediction for the maximum rainfall at any recurrence interval can be

determined. Some of such determination is presented in Table 1.

8

Table 1: Prediction for the maximum rainfall at different recurrence interval

Recurrence Interval (yr) Maximum Rainfall (mm)

10 3400

20 3700

30 3900

40 4025

50 4125

60 4200

70 4275

80 4350

90 4425

100 4500

Minimum Rainfall

The minimum rainfall depth at any desired recurrence can be determined from the long time

series rainfall data. For this, the data has to be ranked in ascending order rather than in

descending order. On these ascending ranking data the Hazens formula is applied. The curve

for the annual rainfall versus recurrence interval is well illustrated in Figure 5.

Figure 5: Annual rainfall versus recurrence Interval for prediction of minimum rainfall at particular

recurrence interval

From this curve, the prediction for the minimum rainfall at any recurrence interval can be

determined. Some of such determination is presented in Table 2.

Table 2: Prediction for the minimum rainfall at different recurrence interval

Recurrence Interval (yr) Maximum Rainfall (mm)

10 2125

20 1800

30 1600

40 1475

50 1400

60 1300

70 1250

80 1200

90 1150

100 1100

9

3.2 Discharge analysis:

Warning level and danger level of flood within Kankai watershed was calculated using the

maximum instantaneous discharge data from 1972-2006. Up to DHM gauge height of 3.7m at

Mainachuli, there is no inundation to the flood plain. So this is the threshold value above which

the flooding begins (DHM, 2010). Hence, the water level at 3.7 m is marked as warning level

and corresponding discharge for the height is about 1900m3/s. Gauge height exceeding 4.2m

causes flooding in the settlements, the inundation depth being greater than 1.5m. Therefore,

guage height of 4.2 m is demarcated as danger level and corresponding discharge for this water

level is about 3250 m3/s. Figure 6 shows the corresponding gauge heights of maximum

instantaneous discharge in comparison to the warning level and danger level at the station. It

can be seen that the warning level is frequently exceeded and the danger level also exceeded at

different times by annual instantaneous maximum floods and the trend of discharge has been

consistently increased since 1995, this might be due to change in precipitation pattern and

change in climate regime of the study area.

Figure 6: Annual instantaneous maximum flood gauge height with warning and danger level

3.3 Flood Frequency Analysis

Flows required for the study have been estimated based on three empirical and one probability

distribution methods. Peak designs have been determined by comparing the four methods with

each other for greater reliability. Modified Dickens's equation, WECS method and Gumbel's

distribution method have been used to determine peak flows. The result of 2, 5, 10, 50, 100 and

200-years Return Period Flood Frequency Analysis based on Maximum Instantaneous flow

recorded at Kankai River(Gaida Station) from year 1972 2003 using Gumbels and WECS/DHM

Method are summarized below in Table 3.

Comparing all the three methods, maximum peak flow is obtained by Gumbel's method.

Therefore peak flows for 25 years designed flood and peak flows of 50 years designed flood are

8007.84 m3/s and 9148.31m

3/s respectively and both the value exceeds the danger level

discharge of 3200m3/s. This data shows that there might be catastrophic flooding on the

watershed because the discharge value for 4.2m height water level is only about 3200m3/s and

it is very small with compared to 25 years designed flood and 50 years designed flood

discharge.

Danger level

Warning level

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Table 3: Flood frequency analysis for various return period

Return Period T Flood frequency

Gumbel's method WECS method

100 10280.36 2874.328

50 9148.31 2575.389

25 8007.84 2299.37

20 7637.63 2149.389

10 6470.53 1827.97

3.4 TIN of Kanaki Watershed

The contour and spot height along with the walkover survey data were used in Arc View GIS to

generate the Triangulated Irregular Network (TIN) of the study area. The observed Elevation of

study area ranged from less than 100m up to 3620m.

Figure 7: TIN of the study area Figure 8: Landuse map of the study area

3.5 Preparation of Landuse Map

The land use map of the Kankai River Watershed derived from the 1992 toposheet along with

field verification is shown in Figure 5. Forest occupies the largest area of the watershed 45.7%

of the total area, while agricultural land occupies about 43.3 % of the total area.

Table 4: Landuse pattern of the study area

S.N Landuse class Area (sq km) %

1 Builtup area 0.02 0.0014

2 Cutting 0.97 0.0755

3 Cultivation 556.07 43.3353

4 Forest 586.48 45.7059

5 Orchard 0.08 0.0065

11

6 Plantation 4.87 0.3794

7 Grass 64.63 5.0371

8 Bush 14.06 1.0960

9 Bamboo 0.06 0.0049

10 Scattered tree 0.02 0.0012

11 Sand 42.67 3.3255

12 Barren area 2.27 0.1769

13 River 10.68 0.8324

14 Pond/ Lake 0.28 0.0218

Total Area 1283.17 100

3.6 Flood Hazard Analysis

The hazard aspect of the flooding is related to the hydraulic and the hydrological parameters.

The results of this assessment are summarized in Table: 3 and Figure 7 & Figure 8 The

classification of flood depth areas indicated that 47.64% & 52.7% of the total flooded areas has

water depths greater than 3 m. The total area under the water depth of 2-3 m was 23.83% on

25 year flood and 23.8% on 50 years flood. Flood hazard maps of the study area for 20-year and

50-year return periods was prepared by overlaying flood grid depths with the TIN. The table

shows that how much area is under high risk, moderate and low risk.

25 year-Return period: Flood Hazard Map 50 year-Return Period: Flood Hazard Map

Figure 9: Flood Hazard map of the study Figure 10: Flood Hazard map of the study

area for 25 year return period flood area for 50 year return period flood

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Table 5: Calculation of Flood Area according to Flood Hazard

Water Depth (m)

Total Flood Area (km2)

25 year flood 50 year flood

Area % Area %

3m (High) 28.25 47.64 31.5 52.7

Total 59.30 100.00 59.8 100.0

The above calculation illustrate that the total area under the water depth of more than 3.0m

increased considerably with the increase in the intensity of flooding. For 25 year flood, it is

observed that >3, 2-3,

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Table 6: Vulnerable areas for 25 years and 50 years flooding

Landuse type Flood Depth Total Vulnerable Area

25 year flood 50 year flood

Area

(km2)

% Area (km2) %

Forest

3 m (High Hazard)

Cultivation 3 m (High Hazard)

River 3 m (High Hazard)

Orchard 3 m (High Hazard)

Sand 3 m (High Hazard)

Grass and

Bambo

3 m (High Hazard)

Barren Land 3 m (High Hazard)

Pond & Lake 3 m (High Hazard)

Builtup area 3 m (High Hazard)

3.8 Vulnerability Assessment of the Study Area by VDC Level

The degree of danger or threat and the levels of exposure and resilience to threat are closely

associated with location. Hence, spatial vulnerability is a function of location, exposure to

hazards, and the physical performance of a structure, whereas socioeconomic vulnerability

refers to the socioeconomic and political conditions in which people exposed to disaster are

living. VDC wise flood hazard mapping is shown in table 7.

14

Table 7: VDC wise flood hazard mapping

VDC Name

25 years return period

Flooded Area in Sq. Km

50 year return period Flooded

Area in Sq. Km

3m 3m

Jitpur1 0 0 0.003 0 0 0.003

Banjho1 0.048 0.003 0.073 0.003 0.015 0.073

Danabari1 0.800 0.473 3.653 0.498 0.580 3.920

Ibhang1 0 0 0.08 0 0 0.080

Mahamai1 1.523 1.638 6.233 1.178 1.435 6.925

Satasidham2 1.230 0.443 2.123 1.078 0.380 2.288

Surunga2 1.930 1.860 5.540 1.558 1.793 5.990

Sarnamati2

1.153 0.715 2.403 1.045 0.700 2.525

Shivjung2

3.060 3.758 1.933 2.383 3.710 2.590

Mahabhara2

0.583 1.935 1.688 0.403 1.875 1.995

Panchgachhi2 0.760 0.158 0.285 0.725 0.190 0.313

Tagandubba2 1.185 0.560 1.333 1.015 0.583 1.480

Kumarkhod2 4.663 2.513 2.935 4.063 2.910 3.400

16.935 14.056 28.282 13.949 14.171 31.582

1, lies in Illam district ; 2, lies in Jhapa district

From the above calculation it is concluded that Kumarkhod, Shivjung, Surunga, Mahamai,

Mahavara & Danabari VDCs are most vulnerable because these VDCs lie in low elevation zone

of the watershed. Floods occur each year in these VDCs, with most of them being normal

floods. Normal floods occur during July, August, September, and they last for approximately 6-7

days. The height of the flood is between 0.5 meter in the village, and up to 3 meters in the

paddy fields. Discussions in both villages revealed that the most recent flood that caused

serious damage occurred in 1990, 1999 & 2008. During these years, about 200 household were

displaced by floods and they still live as flood refugees. Local villagers reported that flood

characteristics have not changed significantly in the last 10-15 years. However, they noted that

damaging floods appear to have occurred with greater regularity in recent years. Losses of

property increased and villagers suffered from flood each year from 1990-2009.

3.9 Socio Economic Analysis:

3.9.1 Demography:

The indigenous ethnicities in the study area are Rajbansi, Tajpuria, Satar, Dhimal, and Gangain.

Besides them, people of other ethnicities are migrated from hills and they are also resided here.

So other ethnic groups in the area include Bramhan, Chettri, Limbu, and Rai.

15

3.9.2 Economic Condition of the affected population:

The majority of people's livelihood is agriculture. It is about 80%. Despite their hard work,

people involved in farming do not have satisfactory living standard. The agricultural land is very

fertile and very suitable for paddy and wheat.

Other than agriculture, fishing, employments in the neighboring towns and India are the

income generating activities of some of the people. Due to low literacy rate, indigenous

ethnicities are back in different aspects compared to other ethnic groups residing in the project

area. Most of the people are suffering unemployment problem.

3.10 Impact of Flooding and Climate Change

Impacts of flooding and climate change on various social variables were analyzed using

participatory vulnerability assessment & semi structured questions among 150 respondents.

Perceptions of local people regarding climate change and flooding were drawn. The general

relation between increasing flooding and climate change were identified through the people's

perception. Altogether six variables were analyzed. Among them, the impact of flooding and

climate change was highest in agriculture productivity through reduction in crop production

mainly paddy and damage of rice fields. The other variable affected were on barren land,

household, livestock. The detail is as shown below in figure 13.

Figure 13: Impact of flooding and climate change

3.10.1 Impacts of Floods

Rice and Paddy Field Losses

CBS 2003 of Nepal states that, more than 80% of people livelihood is based on agriculture and

the economy of the country is also dominated by agriculture. More than 80% people live in

16

rural areas dependent upon subsistence farming, and the economy is based almost entirely on

agriculture with a single season rice crop dependent on the easterly monsoon.

Floods regularly destroy crops in the Terai and midhills of the country. Losing rice stocks and

paddy fields were the single most cited problem for villagers from floods. Paddy field damage is

devastating for some families and exacerbates an already precarious situation in terms of food

security. During one focus group discussion in study area, villagers estimated that 60-70 per

cent of the paddy fields are destroyed. It is not uncommon for some families to completely lose

their rice harvest for the year. Of those families who were categorized as wealthy, or having

enough, the average land area for paddy fields was 5 bighas. Some wealthy families in the

assessment produce 1.5 to 2 tonnes of rice per bigha annually. They may have some rice stocks

to continue feeding the family in case of losses. Those families in the poor category did not

fare as well. They owned an average of 1 to 2 bighas, while at the same time having one

additional member per household to feed than the wealthy families. After losing the rice crop

following a flood, previously landed families take one to two years to find new land. Villagers

said that severe floods occur around every three years, and each time up to 50 per cent of the

households must find new land to farm. They also said that, in addition to being located far

away, the new land may be less fertile, leading to decreased rice production from already low

quantities. Finally, additional help is required to clear the new land. If only one indicator is used

to assess vulnerability from the perspective of villagers, it would be whether a family has a

sufficient amount of rice throughout the year.

Table 8: Rice Sufficiency by Wealth Ranking

Rank Status

Wealthy Enough to sell (3-5 tons)

Middle Enough to eat all year

Poor Short 6 months

Very poor Short 8-10 months

As with most of the other factors of vulnerability that are detailed below, seven VDC of Jhapa

and 4 VDC of Ilam are more vulnerable than other VDC of watershed.

Livestock losses:

Although rice losses and paddy field damage is a common and immediate impact on the

household, most villagers agreed that losing livestock was the most serious blow to long-term

livelihood and family security.

In rural area, the familys buffalos and cows are used as a savings mechanism. When a disaster

strikes or there is a medical emergency, families rely on the sale of livestock for large

expenditures. They act as a safety net and are often the most valuable asset in the household.

Across all wealth categories, the average family lost considerable number of livestock during

major floods. Considering that in rural community one buffalo can buy enough rice to feed four

or five people for an entire year, this is a terrible setback in the familys savings. Cows and

buffalo also play a key role in livelihood as draught animals in the paddy fields.

17

Disease

Following a flood, sanitation is a major concern in the village. People reported a number of

gastrointestinal diseases that would persist for weeks after a flood. This reduces the familys

ability to recover from flood losses and to maintain income. This is particularly a problem when

key labourers in the family become ill. For example, fishing is primarily a male occupation, so

this livelihood activity is closed off when men in the family are sick. People reported there is

increased number of mosquito. And in flood plain a new invasive species has appeared which is

likely to be increased very rapidly, which was not seen before.

House Damaging:

Most of the houses are built high on stilts, and the area beneath the house is used storage.

Nevertheless, 35 of the 150 villagers in the assessment reported housing damage ranging from

slight to total loss. A farmer in Shivjung VDC area said he felt that damage to his hut was the

greatest impact during the 1990 & 1999 flood aside from the destroyed rice. At the time about

96 family became homeless from Simalbadi VDC, Satasidham and Shivjung VDC.

While it is not a common problem within the community, this loss greatly impacts a few

villagers very significantly. They may also be the ones who are poorer and must farm land

farther away than wealthier villagers. The loss of housing at the paddy field takes considerable

resources in time and effort to rebuild the shelters. It is a cost that takes way from other

recovery efforts in farming or supporting the family.

Loss of Forest:

Upper part of watershed is occupied by forest. Some community forest areas are on the side of

river so the flooding has direct impact on forest. Loss of forest might cause direct impact on

local people's livelihood.

Loss of Barren Land:

Most barren land where animal use to graze was destroyed by flooding. From the field survey

and discussion with local people they reflected that most of the barren land or grasslands were

under high risk from flooding. Which has negative impact on their livelihood. Because most of

the people have livestock and livestock use to graze on barren land.

Vulnerability Score

Vulnerability score for the study area was calculated using a standard method provided by

"Participatory Tools and Techniques for Assessing Climate Change Impacts and Exploring

Adaptation Options" a manual published by Livelihoods & Forestry Programme (2010). The

different values were assigned through the questionnaire survey, key informant discussion and

according to people's perception. All the rated values were analyzed using following an

empirical formula. The results show that agriculture system is in much vulnerable.

(Frequency + Area of Impact)*Magnitude = Vulnerability Score

18

Table 9: Vulnerability Score

Sector

Vulnerability

score

Vulnerability in

percentage

Agriculture 50 45.87

Forest 28 25.69

Infrastructure 8 7.34

Biodiversity 2 1.83

Settlement 21 19.27

Different 5 variables were identified from people's perception during field survey. The variables

were than ranked with rating value and final vulnerability score were calculated. From

vulnerability assessment it was found that Agriculture system is much more vulnerable than

other sector.

4. DISCUSSION

The applications of hydraulic model and GIS for floodplain analysis and risk mapping have been

limited in countries like Nepal, where the availability of the river geometric, topographic and

hydrological data are also very limited. The situation of river flooding in Nepal is also

completely different, as there is much higher variation in the river flows and rivers are

completely unregulated. There are very few flood control structures like spurs and dikes and

the river banks and boundary lines are not clearly defined. Hence, the floodplain analysis and

modeling are subject to number of new sets of constraints. This study presents an approach of

conducting a similar study, within these constraints.

HEC-RAS and ArcView GIS were the primary software packages used for this analysis.

HEC-GeoRAS extension facilitated the exchange of data between ArcView GIS and HEC-

RAS.

The spot elevations and contour line are used to prepare the digital terrain model of the

study area so that it can represent the river channel and floodplains adequately.

The flood discharge of different return period is derived by different method. For safe

side the maximum of different methods are taken as flood discharge for the analysis.

During the model run in HEC-RAS, several aspects required were careful considered.

Risk assessment identified the flood prone areas, also the vulnerability in terms of the

type of land use affected and hazard related to the return period of flooding and flood

water depths.

Vulnerability assessment were done from two approach one was GIS based approach

and another was social approach

The social approach was participatory vulnerability assessment, questionnaire survey,

group discussion and key informants discussion. Finally 6 different variables and 5 area

were identified and rating was assigned through a scientific approach.

The results from GIS approach were verified with social approach.

19

5. CONCLUSION

Flood has remained a major problem of the Kankai River for years. These floods have been

reported to be more destructive to the VDCs bordering India. While fire, snakebites, droughts,

epidemics are also prevalent in the lower basin, flood induced inundation, sedimentation and

bank cutting are the major problems of the study area. The main problem associated with

Kankai River is its eroding nature. Valuable agricultural land and residential area is being lost

with each flood season due to bank cutting and water impoundment. It can be stated with

certainty that river is showing avulsion tendencies at some places and meandering in various

place. There is clear indication that the river morphology is changing. The deposition of sands in

the farmland by the torrents originating from the Chure/Siwalik range, inundation due to

flooding and bank cutting at various locations along Kankai River are affecting lives and

livelihoods of the people. This change is related to intensification of human activity in the

catchment area. Such activities are deforestation within the catchment area, unpredicted

weather condition, change in monsoon period, extension of agricultural land into the river flood

plains, extraction of rock, gravel from the river bed and the hills, extreme weather events etc.

Natural process such as change in precipitation pattern and human intervention are the main

causes of flood hazards. A relationship between floodwater and its surroundings is required to

prepare a flood hazard map. The flood hazard map was prepared using HEC-RAS and ArcView

GIS system by comparing with the information on flood hazard gathered in the field.

This study demonstrates a moderate-resolution spatial database for identifying agriculture field

& human settlements that are highly vulnerable to flooding. The lack of high-resolution digital

terrain data for developing countries also leads to difficulty in assessing the accuracy of the

classification results (Sanyal and Lu, 2004). In spite of these constraints, the study has resulted

in reasonably accurate database for flood mitigation. 25 year-return period flood and 50 year-

return period flood hazard maps were prepared. The result shows that 59.3 sq. km & 59.8 of

the study area lie in flooding area in 25 years flood and 50 years flood respectively. Hazard

assessment proved that the area under high hazard zone was considerably increased from

47.64% in 25 years flood to 52.7 % in 50 years flood. From the flood warning and danger level

analysis it was found that the discharge trend has been consistently increased since 1995. The

discharge value obtained from flood frequency analysis for 25 years return period flood and 50

years period flood, both exceeds the discharge value 3200m3/s and lies on danger level and

responsible for flooding on the watershed.

The identification of vulnerable areas was done through social approaches including PVA tools,

key informants consultation and questionnaire survey. The result drawn from both social

approach and from GIS based approach proved that the agriculture system is in most

vulnerable position. More than 10 sq km. of the agriculture land is in high hazard zone. Eastern

districts are major sources of rice for the country. But this result makes the agriculture system

of the country in vulnerable position. Change in precipitation pattern and human intervention

were identified as the main causes of flood hazards. Changes in climate a nd monsoon pattern

made these areas more vulnerable. Special attention of the concerned body on this disaster

should be drawn for the mitigation and to minimize loss from damage.

20

6. RECOMMENDATION:

Hazard mapping should be carried out in a large scale to make an inventory of the

stability of areas such that developmental activities can be placed at proper (stable)

area.

Areas of high hazard need immediate attention, hence appropriate flood protection

measures should be taken earlier.

Conservation work in the watershed

Conservation of the existing forest by involving the local community.

Agro-forestry and conservation farming may be introduced in the upper part of the

watershed.

Grazing land should be properly managed.

Afforestration along banks of stream with gradients less than 150.

7. ACKNOWLEDGEMENTS

This study was supported by NAPA Project under Ministry of Environment; Government of

Nepal. The authors would like to thank Ms. Kareff Rafisura, Mr. Gyanendra Karki, Mr. Post Bd.

Thapa, Mr. Rabin Raj Niraula, Mr. Uttam Paudel, Mr. Tulsi Dahal, Mr. Padam Pandey, Mr.

Anurag Dawadi and Mr. Manik Marki and NAPA team for their continuous support.

Disclaimer:

The findings, interpretations and conclusions expressed herein are those of the author/s and do

not necessarily reflect the view of the NAPA Project/Ministry of Environment, or its

development partners.

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23

Annex-Section

24

Annex B: Process Flow Diagram for using HEC-GeoRAS (Source USACE, 2002)

Start an ArcView

Project

GIS data development

preRAS menu

Generate RAS GIS Import

File

Run HEC-RAS

Enough Cross

Section?

Generate RAS GIS Export

Fille

RAS Results processing

postRAS menu

Correct Inudated

Area?

1. Import RAS GIS export file

2. Generate water surface TIN

3. Generate Floodplain and depth grid

4. Generate velocity TIN

5. Generate Velocity gird

Sufficient Map

detail?

Enough cross

section

Detailed

floodplain analysis

1. Create new project

2. Import RAS GIS import file

3. Complete geometric, hydraulic structure

and flow data

4. Compute HEC-RAS results

5. Review results for hydraulic correctness

1. Create stream centerline a. Label river and reach names b. Attribute theme c. Extract elevation

2. Create Banks theme 3. Create flow path centerlines

a. Label flow path 4. Create/edit land use theme

a. Estimate n-values 5. Create levee alignment

a. Extract/input elevation 6. Create cross section cut lines

a. Attribute theme b. Exact elevations

7. Create ineffective flow areas 8. Create storage areas

a. Extract elevation-volume

Reduce grid

cell size

25

Annex C: profile of Kankai River

Figure C: Plan depicting river centre line and cross-section lines (drawn on the DEM) as well as

the river stations of the Kankai River exported to HEC-RAS environment from ArcView. The

numbers correspond to the cross-section lines.

26

Annex D:

Figure D: Plan depicting river centre line and cross-section lines (3D view) as well as the river

stations of the Kankai River exported to HEC-RAS environment from ArcView. The numbers

correspond to the cross-section lines.

27

Annex E:

0 200 400 600 800 1000180

200

220

240

260

280

kankai Plan: Plan 01 11/13/2010

Station (m)

Ele

vat

ion

(m

)

Legend

EG PF 2

EG PF 1

WS PF 2

Crit PF 2

WS PF 1

Crit PF 1

Ground

Bank Sta

.35 .35

.35

Figure: Cross section of kankai river at 52053.72

0 200 400 600 800 1000 1200140

160

180

200

220

240

260

kankai Plan: Plan 01 11/13/2010

Station (m)

Ele

vat

ion

(m

)

Legend

EG PF 2

EG PF 1

WS PF 2

WS PF 1

Ground

Bank Sta

.35 .35

.35

Figure: Cross section of Kankai river at 43917.62

28

0 500 1000 1500 2000 2500104

105

106

107

108

109

110

111

kankai Plan: Plan 01 11/13/2010

Station (m)

Elev

atio

n (m

)

Legend

EG PF 2

WS PF 2

EG PF 1

WS PF 1

Ground

Bank Sta

.35 .35 .35

Figure: Cross section of Kankai river at 19992.05

0 200 400 600 800 1000 1200 1400 160080

85

90

95

100

105

110

115

kankai Plan: Plan 01 11/13/2010

Station (m)

Ele

vat

ion

(m

)

Legend

EG PF 2

WS PF 2

EG PF 1

WS PF 1

Ground

Bank Sta

.35 .35 .35

Figure: Cross section of Kankai river at 7650.035

29

Annex F1: Average monthly discharge of Kankai river at Mainachuli station (795)

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Average

1972 16.2 14.1 10.5 11.9 14.8 72.5 181 55.5 178 20.1 17.1 12.7 50.4

1973 10.1 8.97 8.1 6.64 13.3 86.9 89.1 175 89.7 149 33.2 20.5 57.5

1974 15.7 11.1 9.4 13.4 23.4 93.6 356 193 151 94.5 38.1 16.6 84.7

1975 10.8 9.33 8.1 9.61 17.2 130 212 86.9 111 43.5 18.2 17 56.2

1976 12.5 12.7 8.14 11.7 17.6 83.7 162 338 102 30.9 12.8 8.9 66.7

1977 7.38 7.87 7.69 32.9 90.4 110 95.8 160 118 99.8 43.4 24.9 66.5

1978 17 10.1 8.46 10.7 23 116 177 81.7 68.1 29.7 20.5 12 47.8

1979 7.66 6.87 5.99 6.09 7.11 18.6 188 130 82.5 43.4 24.9 26.9 45.6

1980 11.8 6.46 6.12 6.13 10.3 34.9 146 158 76.4 31.1 15.7 11.7 42.9

1981 9.58 8.34 7.64 8.22 13.2 31.7 320 174 49.3 27.4 17.3 9.78 56.4

1982 8.38 6.49 5.96 8.33 11.5 66.1 197 110 124 32.5 25.1 13.9 50.8

1983 9.94 9.11 9 9.15 15 25.6 179 79.1 101 48.8 26.6 15.2 43.9

1984 12.8 9.78 8.64 10.3 14.1 63 271 149 149 36.4 21.2 23.3 64

1985 7.87 8.31 7.24 6.81 14.1 41.8 260 ... 82.4 119 21.1 13.7 ...

1986 9.83 6.79 6.77 8.4 11.7 21.9 86.1 64.4 173 41.5 24 18.3 39.4

1987 12.7 10.6 11.6 12.3 12.3 23.8 69.1 564 162 43.5 19 15.6 79.7

1988 14.4 13.2 14.2 13.4 18.7 22 105 259 61.4 27.8 23.8 16.7 49.2

1989 12.8 10.3 9.72 8.17 26.1 66.9 131 76.5 154 45.7 28.8 19.8 49.1

1990 14.3 14.4 13.7 12.2 35.6 82.4 146 235 96.3 45.1 23.3 14.8 61

1991 12.3 7.53 9.28 10 12 93.3 77.2 164 227 29.4 20.3 12.8 56.3

1992 10.5 9.31 7.73 7.24 9.71 15 105 71.7 66.3 36.5 20.7 15.5 31.3

1993 11.5 8.48 7.5 8.25 11.5 36.8 83.8 80.3 57.9 38.7 17.4 8.13 30.8

1994 7.54 7.08 5.56 5.24 6.77 22.3 48.9 110 114 53.2 22.6 17 35.1

1995 13.6 10.9 9.2 8.98 14.7 120 270 184 146 73.5 48.5 21.6 76.8

1996 19.5 15.6 11.8 9.01 15.4 51.6 378 298 130 53.3 16.6 10 84.1

1997 10.9 9.87 8.97 12.3 15 33.4 75.3 119 72.4 23.9 14.5 13.5 34.1

1998 12.9 11 13.5 22.8 32.8 69.3 295 209 151 60.5 39.4 21.9 78.2

1999 17.5 14.7 12.7 13.2 27.8 154 252 291 171 114 45.8 22.2 94.7

2000 16.1 14.3 11.9 23.6 43.8 98.4 167 156 127 32.5 16.7 9.39 59.8

2001 10.5 9.32 8.64 8.95 24.9 40.3 84.7 139 153 209 24.4 13.9 60.5

2002 11.4 10.6 12.6 19.6 33.8 61.2 498 125 94 56.4 22.2 12.4 79.8

2003 11.1 11.3 10.9 10.3 10.7 65.5 361 140 151 109 29 14.7 77

2004 13.3 11.4 11.6 12.6 27.1 98 261 99.1 137 65.8 27.2 14.1 64.8

2005 10.2 8.7 8.74 8.85 11.2 24.2 135 271 74.9 35.6 16.8 10.2 51.3

2006 11.2 10.3 9.65 13.1 19.2 106 189 153 285 77.3 30.2 14.3 76.5

Average: 12.1 10.2 9.35 11.4 20.2 65.2 190 168 123 59.3 24.7 15.5 58.9

30

Annex F2: Peak discharge calculated by Gumbel's distribution

S.N. Year Peak

dischargev(m3/s

) arranged in

descending

order

Rank

M

Probability

of

exceedence

Return

period

T =1/p

Reduced Variants

y=-ln[ln{T-1}]

1 1990 7500 1 0.030 33.000 3.481

2 1981 6430 2 0.061 16.500 2.772

3 2002 6050 3 0.091 11.000 2.351

4 2003 5920 4 0.121 8.250 2.046

5 1974 5700 5 0.152 6.600 1.806

6 1999 5680 6 0.182 5.500 1.606

7 2001 5600 7 0.212 4.714 1.434

8 1983 5500 8 0.242 4.125 1.281

9 1996 5350 9 0.273 3.667 1.144

10 1978 5250 10 0.303 3.300 1.019

11 1979 4900 11 0.333 3.000 0.903

12 1985 4330 12 0.364 2.750 0.794

13 1995 4230 13 0.394 2.538 0.692

14 1987 4150 14 0.424 2.357 0.594

15 1989 3850 15 0.455 2.200 0.501

16 1991 3790 16 0.485 2.063 0.411

17 1972 3690 17 0.515 1.941 0.323

18 1998 3390 18 0.545 1.833 0.238

19 1986 2900 19 0.576 1.737 0.154

20 1994 2900 20 0.606 1.650 0.071

21 1975 2790 21 0.636 1.571 -0.012

22 1997 2700 22 0.667 1.500 -0.094

23 1984 2640 23 0.697 1.435 -0.177

24 1976 2380 24 0.727 1.375 -0.262

25 1973 2360 25 0.758 1.320 -0.349

26 1977 2020 26 0.788 1.269 -0.439

27 1988 1900 27 0.818 1.222 -0.533

28 1980 1780 28 0.848 1.179 -0.635

29 1992 1490 29 0.879 1.138 -0.747

30 2000 1330 30 0.909 1.100 -0.875

31 1982 1000 31 0.939 1.065 -1.031

32 1993 992 31 0.939 1.065 -1.031

Original series Statistical parameters Reduced series

3765.375 Mean y

0.582

1770.660 Standard Deviation y 1.092

31

Cv = / 0.470 Coefficient of variation Cvy= y/ y 1.877

Return

Period T

Reduced

Variants

y=-ln[ln{T-1}]

Frequency factor

T year flood

100 4.6001 3.6794 10280.36

50 3.9019 3.0401 9148.31

25 3.1985 2.3960 8007.84

20 2.9702 2.1869 7637.63

15 2.6738 1.9155 7156.99

10 2.2504 1.5278 6470.53

32

Annex G:

Records of monthly rainfall at Gaida (Kankai)

Latitude (deg/min): 26/35 Longitude (deg/min): 87/54 Elevation (m): 143 Rainfall (mm): Gaida

(Kankai)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1985 0 24 23.5 12 465.5 423.5 902 353.6 241.5 256 15 61

1986 0 0.8 0 29.6 197.5 265.9 581.4 394.8 434.6 163.3 4.6 8.2

1987 0 14.1 79.9 147.7 73.9 471.8 498.6 1370.8 597.3 232.2 7.4 0.2

1988 2.6 27.9 80 143.1 219.8 125.9 820.7 695.9 289 98 31.2 3.4

1989 17.8 23.6 4.6 4 420.7 557.6 810.6 552.3 1141.1 98.8 45.8 2.8

1990 6.2 28.9 24.8 85.7 565.7 782.3 716.7 670.7 459.8 53.1 0 0

1991 12.2 1.5 24.9 50.3 89.3 714.7 236.2 749.5 930.1 172.7 0 9.2

1992 2.2 2.8 0 10.5 215.9 162.1 1014.5 299.8 308 91.4 0 52

1993 23.2 7.8 41.6 118.6 161.1 438.1 958.5 672.1 310.1 320.6 60.3 0

1994 50.6 61.5 23.2 29.9 149.1 401.4 335.9 742.3 413.9 7.8 7.8 0

1995 2.8 13.8 11.4 2.8 233.6 704.7 646.1 458.4 319.5 145.2 74.4 24.9

1996 42.8 12.8 2.6 25.9 290.3 378.9 1248.2 449.6 247.2 137.5 0 0

1997 18.4 6.6 16.7 124.6 92.3 343.2 608.8 349.8 719 8.2 0 45.7

1998 0 2.8 90.1 101.8 119.9 502.6 1054 1347.6 419.5 115.1 8 0

1999 0 0 8.5 43.2 210.5 334.2 837.5 814 508.6 275.2 6.6 0

2000 0 10.6 0 100.2 142.7 422 831.7 745.9 211.9 89.8 28.3 0

2001 1.2 0.9 12.5 98.2 205.5 348.3 462.7 434 403.3 426.8 46.5 0

2002 58 0 12.9 91.4 188.9 450.7 937.3 246.5 195.9 129.7 0 0

2003 0.6 28.2 17.8 41.3 132 428.6 1159.6 381.8 304.1 201.7 34.1 18.3

2004 20.4 0 10.2 93.2 245.3 308.7 903 327.1 412.7 130.7 0 0

2005 23.4 8 49 46.6 110.9 198.6 436.8 757.8 110.2 91.1 0 0

Records of monthly rainfall at Sanischare

Latitude (deg/min): 26/41 Longitude (deg/min): 87/58 Elevation (m): 168 Rainfall (mm):

Sanischare

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1985 0 16.1 32.7 25.7 294 357.1 835.1 414 267.9 364 40.1 0

1986 0 1.5 0 42.6 135.8 404.3 801.1 378.7 302.7 156.8 0 8.3

1987 0 30.1 46.7 42.2 90.3 369.2 625.5 1043.8 674.4 147.9 4.2 0

1988 7.9 49.7 75.2 142.3 231.4 114.3 790.9 905.5 266.9 17.5 32 1.9

1989 45.1 15.5 5.1 8.5 420.3 781.2 624.7 570.1 788.1 172.8 18.9 7.4

1990 0 25.8 41 92.8 357.8 554.4 583.1 637.7 584.7 72.5 0 0

1991 16.2 0 16.5 86.7 94.3 924 478.2 664.8 897.2 71.6 0 10.8

1992 0.4 3.4 0 8.4 175.6 278.2 1132.6 377.2 274.7 111.5 0 14

1993 18.6 29.8 25 66 178.9 738.4 762.7 546 296.8 229.7 104.2 0

33

1994 62.1 96.8 64.2 107.2 197.8 266.5 440.6 323.2 420 2.6 11.8 1

1995 0 23.2 15.8 9 359.2 1002.1 847.3 686.5 313.6 118.6 73.2 22.2

1996 49.4 19.4 9.2 16 277 483.7 1153.5 592.4 187.9 72.5 0 0

1997 0.8 15.6 40 91.9 69.4 390.4 613.7 393.7 504.1 7.6 13.2 53.1

1998 0 1.2 122.4 107.2 83.2 756.6 1052 872.4 342.4 88.2 0 0

1999 0 0 7 66 140.2 439.3 661 1005 384 162.6 34.5 0

2000 0 16.6 0 166.7 431.2 680.2 781.2 768.5 163 73.6 23.2 0

2001 DNA 12.6 9 60.6 258.4 372.4 555.4 424.1 535.2 518.9 109.6 DNA

2002 49.8 0 32.8 83 184 415.4 1135.7 314.8 226.6 129 0 0

2003 10 53.5 30.2 46.4 62.6 545.2 1221.2 515 408.6 244.7 0 0

2004 22.6 0 13.4 130.6 112.8 509.4 1003.4 364.7 422.3 153.4 0 6.6

2005 34.2 8.6 43.3 74 99 231.2 458.8 639.8 110.2 92.4 0 6.6

Records of monthly rainfall at Damak

Latitude(deg/min):26/40 Longitude(deg/min): 87/42 Elevation(m): 163 Rainfall (mm):

Damak

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1985 3.5 1 0 67.1 361 501.8 775.3 137.1 297.4 224.5 13.5 46

1986 0 0 2 40 155.2 333.9 466.6 457.5 317.9 139.5 0 5

1987 5 11.5 32.6 50.5 78.5 452.6 567 1118.2 459.6 204.5 0 0

1988 5 42.5 90.5 117.2 225 151.3 661.6 565 274.7 36.7 20.5 3.4

1989 10 47.5 12 0 314.5 573.5 962.7 497.7 821.8 79.3 11 0

1990 0 28.5 76.2 48.5 382.8 486.1 501.6 622.6 324 78 0 0

1991 11 0 1.5 29.4 68.6 550.9 242 706 536.6 190.7 0 4.3

1992 2 0 0 5.1 144 163.8 787.8 307.1 230.6 149.2 0 15.1

1993 20.1 7.1 17.5 34.8 203.8 246.3 535.7 561.5 190.1 225.2 28.2 0

1994 68.4 69.4 39.1 28.9 116.7 275.1 314.3 458.1 215.6 0 0 0

1995 0 0 21.2 125.6 233.5 551 708.7 527.8 356.6 159.9 99.2 15.3

1996 18.7 10.9 0 14.2 271.4 355.3 1224.7 536.4 285.2 74.2 0 0

1997 0 10.8 18 66.9 133.4 369.8 428.3 266.3 690.6 DNA DNA DNA

1998 94.2 5.4 85.5 122.1 151.3 522.8 919.4 834.2 153 148.1 22.9 0

1999 0 0 0 12.8 243.6 372 843.9 798.3 372 81.3 16.9 0

2000 21 17.7 0 122.5 349.8 740.6 600.3 905.8 210.7 61.3 30.5 0

2001 0 0 16.3 79 174.6 179.1 282 437 582 462 13.6 DNA

2002 64.2 3.1 35.2 125.3 137.3 313 931.7 94.6 124.6 49.2 0 0

2003 10.3 34.8 28.9 49.9 113.3 364.3 791.7 244.5 240.1 102.5 0 69.5

2004 32 0 12.5 112.6 198.7 406.2 703.1 207.9 476.8 159.7 1.8 0

2005 9.7 7.6 80.9 54.3 76.4 214.3 273.3 748.1 113.9 77.1 0 0

34

Records of monthly rainfall at Ilam Tea Estate

Latitude(deg/min):26/55 Longitude(deg/min): 87/54 Elevation (m):1300 Rainfall (mm):

Ilam Tea Estate

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1985 0 29 2 32.5 143 205.5 732 170.4 263.5 199 7 25

1987 5 33.5 75 72 135 250 448 857.5 469.5 173 26 4

1988 6 24 39 78.5 200 108.5 544.5 481.5 100.8 10 49 0

1989 44 46.5 24.5 9.5 193.5 428 511 234.5 557 48.5 3 11.5

1990 0 56 46.5 58 205 423.5 370.5 349 367 62 0 0

1991 50 2.5 17 30 106 496 263.5 370 453 0 0 12

1992 3.6 22 0 42 132.4 211.1 556.4 197.5 212.3 28 0 1

1993 21.5 3.4 11.8 90.5 191.8 313.3 369.3 347.6 233 50.6 16.3 0

1994 33.6 39.2 7.5 16.3 148.6 281.2 314.9 288.4 135.6 0 2.6 3

1995 13.6 13.8 34.8 18.4 88.4 464.8 573.8 435.3 277 17.4 155.8 14.4

1996 42.8 6.2 27.2 51.4 61 295 545.6 520.7 100.9 30.8 0 0

1997 10.4 27.2 10.4 174.6 49.6 249.4 230.9 642 448.8 4 0 70.8

1998 0 2 124.4 166.9 99.2 329.9 561.5 418.8 224 106.8 21.8 0

1999 0.6 0 0 38.5 122.6 440.2 516.6 567.4 266.2 124.2 0 1.4

2000 8.2 0.1 0 54.4 257.2 379.6 309.8 332.2 186.7 16.4 0 0

2001 0 24.5 0 34.5 97.2 168.4 254.2 230 188.5 333.4 0 0

2002 23.6 0 13.4 65.6 92.8 303 584.1 347.4 83.2 28.6 0 0

2003 16.6 41.2 33.4 79.8 73.6 387.1 784.2 297.4 101.9 148.8 0 36.6

2004 27.4 0 DNA DNA DNA DNA DNA DNA DNA DNA DNA DNA

2005 DNA 0 30 50.4 119.9 DNA 290.6 DNA 41.9 60.2 0 0

Annex: H

Topographic survey data

X Y Z Remarks X Y Z Remarks X Y Z Remarks

587,537.50 2,949,150.00 120.000 K1 587499.236 2949734.438 111.200 587939.563 2950092.773 113.266 Bank bottom

586,870.58 2,949,372.31 120.000 K2 587521.788 2949732.557 111.374 587905.446 2950101.777 113.486 Sand

587,522.18 2,949,155.11 112.989 Bridge botam 587525.737 2949732.227 111.364 587889.050 2950106.105 113.396 Sand

587,512.78 2,949,158.24 112.330 587532.850 2949731.634 111.699 587870.289 2950111.056 113.369 Sand

587,508.09 2,949,159.80 110.299 Paire 587551.027 2949730.118 112.092 587824.239 2950123.210 113.212 Sand

587,479.31 2,949,169.40 110.295 587570.013 2949728.534 111.796 587781.192 2950134.572 112.785

587,452.47 2,949,178.34 109.916 587597.867 2949726.211 111.742 587772.173 2950136.952 112.896

587,426.11 2,949,187.13 110.172 587626.524 2949723.820 111.699 587762.794 2950139.427 112.916

587,397.58 2,949,196.64 110.396 586937.570 2949781.288 112.753 587722.329 2950150.107 112.822

587,369.83 2,949,205.89 110.925 586967.386 2949778.801 112.487 587678.966 2950164.558 112.184

587,343.40 2,949,214.70 109.442 586996.998 2949776.331 112.387 587691.287 2950158.869 112.417

587,314.12 2,949,224.46 109.838 587026.877 2949773.839 111.790 587659.917 2950173.354 111.116 Water level

587,286.31 2,949,233.73 109.836 587037.836 2949772.925 111.773 587612.016 2950195.472 111.110

587,266.32 2,949,240.39 109.281 Water level 587056.791 2949771.344 111.741 587584.599 2950208.132 110.529

587,239.81 2,949,249.23 109.322 Water level 587086.986 2949768.825 111.757 587532.501 2950232.188 111.137 Water level

587,635.76 2,949,723.06 111.761 k-3 587116.565 2949766.358 111.817 587528.603 2950233.987 113.083 Bank top

587,635.76 2,949,723.05 111.786 k-3 587136.084 2949764.730 112.457 586998.634 2950478.697 116.294 Bank top

587,175.69 2,949,270.60 110.504 Water level 587157.220 2949762.967 111.656 587003.550 2950476.428 113.513 Bank bottom

587,148.76 2,949,279.58 110.707 Water level 587187.202 2949760.466 111.574 587031.302 2950463.613 113.430

587,120.09 2,949,289.14 110.240 Sand bar 587226.765 2949757.166 110.791 587057.361 2950451.581 112.629

587,092.78 2,949,298.24 110.295 587246.482 2949755.521 111.613 587067.159 2950447.057 112.530

587,064.83 2,949,307.56 110.372 587286.861 2949752.153 110.857 587103.599 2950430.231 112.516

587,037.12 2,949,316.79 110.131 587345.786 2949747.238 111.888 587139.453 2950413.675 112.400

587,010.02 2,949,325.82 110.189 587667.951 2949720.363 111.655 587153.080 2950407.383 112.320

586,981.60 2,949,335.30 110.353 587688.349 2949718.661 110.877 587176.499 2950396.570 112.300

586,954.61 2,949,344.30 110.267 587727.069 2949715.429 112.203 587203.222 2950384.230 112.100

586,927.05 2,949,353.48 110.129 587750.011 2949713.514 112.338 587231.039 2950371.386 112.094

586,900.25 2,949,362.42 109.264 587771.968 2949711.682 112.256 587249.133 2950363.031 112.003

586,902.17 2,949,361.78 109.755 587804.991 2949708.926 112.372 587275.627 2950350.797 112.004

586,868.48 2,949,373.01 120.295 Bridge top 587835.336 2949706.393 112.641 587321.552 2950329.592 111.994

587862.925 2949704.090 113.972 Bank top 587357.867 2950312.824 111.600

587537.507 2949150.012 119.941 587875.611 2949703.032 113.968 Field 587385.374 2950300.123 111.589

587734.068 2950296.078 119.970 586991.714 2949776.803 112.434 river 587412.538 2950287.580 111.259

587694.166 2950157.557 112.530 k4 587022.891 2949774.201 111.864 587439.722 2950275.028 111.529

587694.163 2950157.541 112.522 k4 587466.868 2950262.493 111.590

586909.052 2949783.667 115.430 Bank top 587635.761 2949723.060 111.700 k-3 587475.471 2950258.521 111.765

587451.327 2949738.434 110.016 Bed level 587752.565 2950592.021 111.692 k-3 587503.797 2950245.441 112.005

587356.344 2949746.357 110.674 Water level 587765.932 2950598.412 112.164 k-5 587530.637 2950233.048 111.905

587467.964 2949737.046 110.607 Water level 587765.931 2950598.411 112.180 k-5 587765.932 2950598.412 112.164 k-5

587485.884 2949735.552 110.714 Water level 587979.392 2950082.261 114.883 Bank top 587694.159 2950157.515 112.374 k-4

MadanTypewritten Text

MadanTypewritten Text35

Topographic survey data

X Y Z Remarks X Y Z Remarks X Y Z Remarks

587694.137 2950157.517 112.374 k-4 588070.175 2950893.991 113.828 587964.455 2951427.278 115.201 k-7

587924.667 2950940.896 114.009 k-6 (L) 588043.455 2950902.605 113.699 587964.454 2951427.277 115.198 k-7

587607.180 2950255.934 114.016 k-6 (R) 588018.132 2950910.767 113.008 588162.367 2951995.242 114.898 Water level

588168.649 2950459.438 117.042 Bank top 587989.863 2950919.880 112.626 Water level 588200.889 2951995.205 115.573

588141.246 2950468.895 114.234 Bank bottom 587953.218 2950931.692 112.158 Bed level 588233.508 2951995.174 119.152

588106.453 2950480.901 114.386 Sand 588272.915 2951995.137 120.614

588063.321 2950495.786 113.946 Sand 587924.659 2950940.803 113.845 588415.089 2951995.002 122.351

588043.153 2950502.746 113.827 Sand 587924.648 2950940.806 113.856 588320.694 2951995.091 125.351

587998.629 2950518.111 113.747 588024.708 2951995.372 117.550 k-8 588109.835 2951995.291 114.905 Water level

587954.780 2950533.243 113.275 587826.756 2951981.649 117.581 k-8 588085.577 2951995.314 115.571 Sand

587914.200 2950547.246 113.469 587949.282 2951431.800 115.039 588063.059 2951995.336 115.930

587857.145 2950566.935 113.462 587914.947 2951442.086 114.558 588038.991 2951995.358 115.016

587817.603 2950580.581 112.160 587865.566 2951456.879 114.923 588032.681 2951995.364 115.011 Sand

587786.512 2950591.310 112.149 587877.585 2951453.279 114.768 588013.764 2951995.382 117.787 Sand

587755.974 2950603.941 111.954 Water level 587841.214 2951464.174 116.992 Bank 588009.328 2951995.387 117.453

587739.338 2950613.177 110.219 Water level 587820.101 2951470.499 117.092 Bank 587994.518 2951995.401 117.503

587724.666 2950621.323 112.004 Water level 587781.496 2951482.064 117.185 587970.551 2951995.423 117.415

587691.610 2950639.676 112.672 Bank bottom 587717.197 2951501.326 116.885 588155.368 2952429.047 121.743 Dam

587688.086 2950641.632 114.816 Bank top 587705.539 2951504.818 116.764 587949.696 2951995.443 117.814

587685.731 2951510.752 117.044 588123.247 2952505.475 123.273

587765.956 2950598.462 112.075 k-5 587658.883 2951518.794 117.092 587896.950 2951995.493 118.691 House

587765.966 2950598.464 112.071 k-5 587623.727 2951529.326 117.311 588003.334 2952464.335 127.337

587964.452 2951427.256 115.321 k-7 587586.955 2951540.342 117.499

587964.441 2951427.261 115.321 k-7 587562.004 2951547.816 117.770

587942.017 2950935.303 112.620 Water level 587536.713 2951555.393 117.860 585937.500 2960737.500 150.000 k-100

587895.547 2950950.283 114.001 Bank 587489.802 2951569.446 121.517 586429.678 2961250.882 153.644 Lodiya Khola & road

587940.544 2950935.778 113.628 Bank 587485.787 2951570.648 121.480 585714.854 2961060.818 153.644 k-101

587863.404 2950960.644 113.817 Bank 587980.477 2951431.051 115.251 586300.526 2960338.263 149.045 k-99

587821.877 2950974.031 113.939 Bank 587993.633 2951434.167 114.852 586300.532 2960338.276 149.006 k-99

587773.285 2950989.694 114.066 Bank 588060.854 2951450.086 114.747 586354.522 2961253.572 153.626 x- section at axis thought river

587764.250 2950992.607 115.326 Bank 588071.181 2951452.532 114.354 586300.038 2961252.723 152.946 Road cl

588346.046 2950805.064 117.663 Bank top 588110.345 2951461.807 114.252 586286.427 2961233.379 152.694 Field

588319.216 2950813.713 114.634 Bank bottom 588145.135 2951470.046 113.840 586271.846 2961212.657 152.242 Field

588276.958 2950827.335 114.950 Sand 588177.219 2951477.644 113.715 Water level 586249.946 2961181.534 152.097 Field

588232.084 2950841.800 114.960 Sand 588196.713 2951482.261 113.243 Water level 586237.266 2961163.514 151.916

588172.136 2950861.124 115.160 Sand 588225.516 2951489.082 113.743 Water level 586219.700 2961138.549 151.886

588201.333 2950851.712 115.168 588234.798 2951491.280 115.882 Bank 586228.602 2961151.201 151.799

588146.866 2950869.270 115.175 588270.964 2951499.845 116.482 Bank 586195.201 2961103.733 151.651

588105.885 2950882.480 114.918 588308.591 2951508.756 119.034 Bank top 586167.707 2961064.660 151.412

588078.994 2950891.148 114.939 588332.071 2951514.317 119.056 Field 586133.980 2961016.729 150.681

MadanTypewritten Text36

Topographic survey data

X Y Z Remarks X Y Z Remarks X Y Z Remarks

586090.079 2960954.338 151.172 586643.013 2959963.571 147.434 k-98 587080.712 2960363.649 150.923 Bank

586063.950 2960917.205 150.243 586643.005 2959963.561 147.393 k-98 587071.857 2960355.555 148.983 Bank bottom

586049.453 2960896.602 150.309 585923.434 2959995.374 150.020 Bank bottom 587061.222 2960345.834 147.664

586019.336 2960853.802 150.869 585900.493 2959974.513 155.225 Bank top 586143.509 2959757.397 147.620