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Project Number: 45206 May 2016

Grant 0299-NEP: Water Resources Project Preparatory Facility

Final Report – Volume 1.2

Prepared by

Lahmeyer International in association with Total Management Services Pvt. Ltd.

For Ministry of Irrigation, Government of Nepal Department of Irrigation, Government of Nepal

WRPPF-Package 3: Flood Hazard Mapping & Preliminary Preparation of Risk Management Projects Final Report May 2016

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Volume 1: Main Report Lahmeyer International in association with Total Management Services

Table 16: Matrix of Flood Risk Scores

Flood Hazard Rating

Flood vulnerability/Societal vulnerability

Settlement Cultivated Forest, bare

River bed/ water

body

Weight 9 2 1 0

Extreme 4 36 8 4 0

Significant 3 27 6 3 0

Moderate 2 18 4 2 0

Low 1 9 2 1 0

211. The flood risk scores are then reclassified into simpler categories to describe the flood risk. Table 17 shows the reclassified flood risk thresholds.

Table 17: Flood Risk Thresholds

Flood Risk Category Flood Risk Score

Extreme/High >= 9 Moderate 4 – 8

Low =< 4

212. In discussion with the DWIDP, flood risk maps were generated for each basin for the 50 year plus climate change scenario which is the adopted return period for flood mitigation works and these are presented in Appendix C. Figure 21 shows the flood risk map for Mohana Basin.

D. Greenbelt

213. To align with the recent DWIDP Water Induced Disaster Management Policy and taking into account the effects of climate change, a greenbelt has been defined where personal and settlement development would be prohibited and allowable uses would be confined to agriculture, vehicle parking and recreational parks. This reflects the DWIDP aspirations to designate a zone Z1 which is bound by the 5 year return period flood extent. Figure 22 shows the greenbelt area which corresponds to the 5 year plus climate change flood extent.


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Figure 18: Maximum Flood Depth - 1 in 50 years plus Climate Change - Mohana Basin


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Figure 19: Maximum Flood Velocity - 1 in 50 years plus Climate Change - Mohana Basin


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Figure 20: Maximum Flood Hazard - 1 in 50 years plus Climate Change - Mohana Basin


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Figure 21: Maximum Flood Risk - 1 in 50 years plus Climate Change - Mohana Basin


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Figure 22: Greenbelt - Mohana Basin


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VII. BASIN SCREENING

A. Introduction

214. The 25 study basins were required to be screened to select 6 priority basins in which flood intervention measures are to be developed upto pre-feasibility level concept notes.

215. This was done by ranking the basins according to the likelihood and magnitude of flood damage and other parameters including poverty index, impact on human wellbeing, loss of life and property and damage to infrastructure. Other parameters were later considered in conjunction with the Client. The following procedure was followed in the screening process.

216. A database of reported flood events was compiled from Ministry of Home Affairs (MOHA) data available on the internet (http://www.neoc.gov.np) covering flood events from 1991 to 2010, and files supplied directly to the study team by Ministry officials covering the period 2011 to 2015. 1,624 flood events for the period 1991-2015 were found to occur in District and VDC areas occurring within the 25 study river basins. This is an area of about 9.36 million ha. Of the 2,177 VDC that are located or partly located in this area, 805 (37%) were found to have experienced a reported flood event since 1991.

217. The flood record database was subsequently refined to the “modeled area” of the 25 study basins. This is in the lower parts of the basins that are prone to the aggressive downstream flooding for which flood hazard maps are required to be prepared. Confining the study of historic flood events to this area allows a more specific characterization of flood impact and therefore will improve the design of flood management interventions. The modeled area is about 1.75 million ha. 693 Village Development Committees (VDCs) are located or partly located within the area. The database of flood events showed that 408 of these VDCs (59%) have experienced a reported flood event since 1991.

218. The flood record database was interrogated to rank flood events in the 25 river basins. The ranking took into account the frequency and magnitude of all reported events. Then specific components of flood damage were identified, including impact on human wellbeing (loss of life, injuries, displacement etc.) and direct damage to property, crops, livestock and infrastructure. The magnitude of reported events was then defined by generating a score based on reported damage by each component to identify high, very high and extreme flood events over the 24-year period. Clearly this classification rests solely on an assessment of the vulnerability of the basin population to flood events. No hydrological data is available to estimate risk (flood magnitude and return period) associated with each event.

219. The durability of the ranking was then investigated using independent socio-economic explanatory variables including population, urbanization, the quality of housing and poverty indices. The ranking was found to be reasonably robust in that an acceptable proportion of the variation of flood damage between basins could be explained by some simple and independent (non-auto correlated) variables.

220. Finally, the Client’s local knowledge and insight of the developments on the ground were incorporated in the screening process. This introduced such practical considerations as the presence of on going or already planned projects, the importance of an equitable geographical distribution of interventions, and the potential to protect high priority locations such as regional centers.

http://www.neoc.gov.np/


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B. The Flood Record Database

221. The MOHA dataset usually includes a specific description of the location of flood impacts, in many cases down to Ward level. Each of these locations has been cross-referenced with the Project GIS, which allows each flood record to be identified by river basin. In some cases the VDC boundary extends over two basins, and clearly VDCs do not always fall entirely within the basin areas. The GIS was used to calculate the area of each VDC and Ward inside the study area and within each basin, and the total area of each VDC and Ward is available from CBS. One important limitation of the MOHA data is that some single flood events are reported for multiple VDCs. The information given is often insufficient to disaggregate them.

222. The Project database includes all those records that were specified as “floods” in the MOHA data, though it is possible that some floods were partly from excessive local rainfall rather than over-topping of riverbanks. The earlier records (pre-2011) include a narrative on the event and the cause. The date of each flood record is specified in the MOHA dataset, as is the flood duration if it was significant. Confining the dataset to the Terai means that disasters associated with flooding (for example landslides) are less frequent.

223. Direct damage from each flood event is reported, including loss of life and injuries and displacement of population. The impact on housing is also reported, focusing on the number of houses destroyed and damaged. Often the area of agricultural land affected is reported in detail. Earlier flood records make a qualitative assessment of flood impact on infrastructure. Only about 15% of reported flood events include an estimate of financial losses, which are made in current NPR.

224. Comments and source of information on the flood event are preserved in the database; these often provide useful additional and supporting information.

225. For the screening study, the estimated value of losses as reported by MOHA was deflated for each year (using the IMF GDP deflator) and given in constant 2015 prices. The quantities of damage (loss of life, numbers injured or displaced, areas of arable land affected and livestock deaths) were used as reported in the MOHA data.

C. Frequency of Flood Events by Basin

226. Table 18 shows the number of reported flood events by modeled basin area over the period 1991-2015. There are 1,089 events reported by administrative area in the database. Some of these administrative areas have their boundaries crossing two basins. The number of reported events were used as a criteria to rank the basins.


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Table 18: Frequency of Reported Flood Events by Basin, 1991-2015

Basin SN Basin name Number of Reported Flood Events

% of total events Ranking

17 Narayani 224 17% 1

16 East Rapti 143 11% 2

15 Lal Bakeya 135 10% 3

20 Karnali 81 6% 4

14 Lakhandehi 69 5% 5

19 West Rapti 60 5% 6

4 Mawa Ratuwa 58 4% 7

9 Balan 54 4% 8

24 Dodha 49 4% 9

2 Kankai 48 4% 10

1 Biring 43 3% 11

6 Chisang 38 3% 12

13 Jhim 38 3% 12

3 Kamal Baniyani 38 3% 12

8 Khando 34 3% 15

10 Gagan 33 2% 16

12 Aurahi/Bighi 32 2% 17

11 Jalad 31 2% 18

23 Mohana 30 2% 19

5 Bakraha 29 2% 20

7 Budhi 19 1% 21

18 Banganga 18 1% 22

25 Chaudhar 16 1% 23

22 Kandra 11 1% 24

21 Khutiya 1 0% 25

TOTAL 1,332

227. It is clear from the Table 18 that the larger basins tend to have the bigger numbers of reported flood events, which is as expected. But without corresponding physical information (catchment condition, hydrology etc.) it is not possible to construct a model to assess the risk of the reported flood events. However, the flood event database does allow an assessment of vulnerability because flood damage is known.

D. Reported Flood Damage as an Estimate of Vulnerability

228. Table 19 shows the number of events that exceeded defined levels of damage by basin. For example, in East Rapti there were 10 events reported in the period 1991-2015 with a level of damage of over NPR 1 million in 2015 constant prices. Also in East Rapti; 22 events had at least one fatality in the same period, and so on. The permitted level of damage per event (reported damage over NPR 1 million, more than one death etc.) was assessed subjectively by inspecting the frequency distribution of each damage component. There is a small element of double counting introduced by including the estimated financial cost of flood events, but since cost is available for only 16% of the flood records and contributes only 10% to the score of magnitude, so the bias cannot be strong.


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Table 19: High Impact Flood Events by Basin, 1991-2015


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229. The magnitude assessment is simply a summation of these occurrences, and should not be confused with the frequency of flood events reported in Table 18. The measure is not a frequency but a measure of impact. A composite measure is necessary because floods have different characteristics that cause different types of damage. Another reason is that damage is measured subjectively in the MOHA dataset – if the flood destroys many houses then agricultural damage might be downplayed or ignored in favor of reporting the more significant impact. The contribution of damage components of the measure is shown in the bottom row of Table 19. The components with greatest weight in the measure are numbers of affected people (18%), house destruction (17%) and arable land affected (20%)

230. The final column of the Table 19 ranks the basins in order of magnitude impact. The incidence of flood events with significant levels of damage is clearly greatest in East Rapti, Lal Bakeya and Lakhandehi. It shows that East Rapti and Lal Bakeya both had a high frequency of reported flood events. Lakhandehi had fewer, but a greater proportion of these were relatively damaging. One might suggest that the vulnerability of this basin compared to East Rapti and Lal Bakeya is relatively high.

231. Karnali and Narayani both experience floods of lower damage magnitude – the numbers of people reported as affected per flood event is much lower.

232. A similar exercise was carried out to identify Very High and Extreme Magnitude flood events, simply by raising the criteria of damage to qualify as a damage event. Details are given in Appendix D. Again, the criteria were selected by inspecting the frequency distribution of level of damage by each component. The reported damage is required to be greater than NPR 10 million to be included in the category of a Very High Magnitude flood. To qualify for Extreme Magnitude classification reported damage must be over NPR 100 million. Extreme events are identified only by the highest damage reported in each component. 41 of them have occurred in the 25 basin modeled areas in the last 24 years and of these 40% of those were in East Rapti. However, East Rapti did not experience the level of fatalities reported during floods in Naryani and Chisang.

233. The rankings for high, very high and extreme events, as well as the rankings for frequency of reported flood events, are shown in Table 20. By summing the frequency and magnitude assessment score a composite ranking (column 6) can be calculated and grouped according to high (red), medium (yellow) and low (green) flood impact.


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Table 20: Consistently High Ranked Basins Flood Frequency and Magnitude Estimates

234. The rankings of frequency and magnitude of flood events show that five basins consistently appear in the upper ranking with highest flood impact: East Rapti, Narayani, Karnali, Lal Bakeya and Lakhandehi, in that order. Chisang, Khando and Biring have a relative low frequency of floods but those that occur are relatively damaging. Khutiya, Kandra, Budhi, Bakraha, Gagan and Banganga are in the lowest ranking with apparently least flood impact.

E. Socio-economic Characteristics of Basin Modeled Areas

235. It is also worth assessing the vulnerability of population by basin to flood events. However, without statistics by VDC the characterization must be based on District-level statistics compiled by basin. Statistics may be misleading because inter-District variation between VDCs and wards is not taken into account; an inaccuracy that is compounded by weighting District level data to derive a basin-level statistic. In general the socio-economic characteristics of flood affected VDCs may differ from VDCs unaffected by floods. More importantly, the socio-economic status of populations in flood-affected wards is likely to be different from the VDC because it is marginalized to areas of greater risk.


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236. Table 21 gives a summary of selected statistics by basin that include population, housing, poverty and land use – characteristics that are expected to determine vulnerability to flood events. The population in modeled basin areas (1.7 million ha) is estimated to be 4.5 million, or 18% of the population of Nepal, but only a relatively small percentage lives in directly flood-affected areas. At over 300 persons per km2 the population density in modeled basin areas is high; the average for Nepal is 190 persons per km2. Overall there are only five members in the average household, a statistic that does not vary much between basins. The average for Nepal is 4.88. The sex ratio is only 83 males to 100 females, while the average for Nepal is 94, suggesting a substantial and disproportionate out-migration of male labor from the modeled basin areas. Population growth is variable in the basin modeled areas, in the period 2001-2011 it averaged 1.67% per annum compared with the average for Nepal 1.35% in the same period.

237. There are about one million houses in the modeled basin areas and, according to the Population and Housing Census 2012, about 50% of these have poor quality foundations and 37% have poor quality walls.

238. The Human Poverty Index (HPI) in the modeled basin area is estimated to be 30, compared with an overall estimate of 31 for Nepal as a whole The Human Development Index HDI) in the modeled basin area is estimated to be 47, compared with an estimate for Nepal as a whole of 49 (Nepal Human Development Report, UNDP 2014).

239. Some modeled basin areas have significant urban populations. It is appears that about 15% of the population in the area as a whole lives in VDCs classified as municipalities. Agriculture in the modeled basin areas is represented by the wet season cultivated area, obtained by District from the national Sample Agricultural Census of 2011. Livestock numbers were obtained in the same way.


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Table 21: Selected Socio-economic Indicators by Basin Modelled Area


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240. A simple ranking system can be applied to the data in Table 21, which is shown in Table 22. Visual inspection suggests that correlations between socio-economic indicators expressed as a ranking by basin modeled area and the flood impact ranking do not seem strong. This is to be expected: the rankings for statistics of HPI, HDI and agriculture are based on District statistics and may have little relevance for flood-affected areas. Population (total and urban) and house quality statistics are however derived by VDC.

241. The relationship between HPI and HDI in Table 22 needs explanation. High poverty is expected to be an exacerbating factor of vulnerability to flooding. It is ranked so that a ranking indicating high poverty is 1, or bad. High development may also indicate vulnerability to flooding, with greater losses of assets, but in this case a low development index receives a high rank. This explains why the majority of basins have similar colors for HPI and HDI.

242. A combined selection rank merges the flood impact rank with the ranking of key socio-economic indicators (excluding HDI, which is ranked the same as HPI) by summing scores and re-ranking

Table 22: Ranking of Socio-economic Indicators by Basin

243. The color code in column 2 indicates the regional distribution of the basins: Eastern (1-10), Central (11-17), Western (18), Mid-Western (19-20) and Far Western (21-25). The color code in column 1 indicates the new regional distribution following the adoption of the constitution on 25 September 2015. New Province 1 covers basins 1-7, New Province 2 covers basins 8-15, New Province 3 covers basin 16, New Province 4 covers basin 17, New Province 5 covers 18-20 and New Province 7 covers basins 21-25.

F. A Test of Robustness of Ranking

244. The evidence suggests that, solely on the basis of flood frequency and magnitude of damage, East Rapti, Narayani, Karnali, Lal Bakeya and Lakhandehi basin modeled areas would be chosen, in that order. Chisang, Khando and Biring have a relative low frequency of floods but those that occur are relatively damaging. These areas would also merit consideration.

245. Based on socio-economic indicators, East Rapti, Narayani, West Rapti, Banganga,


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Lal Bakeya and Karnali have high basin populations and would therefore receive a high priority for flood management. Basins in the Central Region have the highest HPI and the lowest HDI. Biring and Kankai both have poor quality housing. East Rapti and Mohana both have significant urban populations. Lakhandehi and East Rapti appear to have agricultural assets that may be vulnerable to floods.

246. It is worth trying a test of the robustness of the classifications used. It is impossible to test for a physical explanation for flood frequency and magnitude without hydrological data specific for each flood event. However, relative vulnerability (as measured by the number of high impact floods) of the 25 basins can be tested using the independent socio-economic explanatory variables following the hypothesis that vulnerability to flood impact can be explained by numbers of people in flood affected areas (population), the difficulty of escaping in a flood event (urban population), the quality of construction to resist flood damage (house quality) and the immovable assets which may be damaged (housing and agriculture).

247. The magnitude of flood impact (expressed as the number of high impact events in the period 1991-2015) was regressed on the independent variables of basin population in 2012, the number of livestock per basin and the quality of housing. The number of houses is clearly auto-correlated with population and was omitted. The results are described in Table 23 and suggest that basins with high population density, smaller numbers of livestock (reflecting asset poor status of the population) and poor quality housing may experience flood events with a higher magnitude of damage. The coefficients are significant at 5%, with the exception of that of livestock numbers, which is significant at 10%. Population and quality of housing explain the largest variation in flood damage magnitude, which is intuitively reasonable.

Table 23: Linear Regression: Flood Magnitude Regressed on Population, Quality of Housing and Livestock Numbers

248. Bearing in mind the independent variables are derived at District level, whereas the flood magnitude estimate is derived at VDC level, the level of explanation is surprisingly good (R2=0.73, F=22.5, significant at 99%).


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249. Other independent variables (derived from District level data) were tested to explain flood magnitude, including wet season cultivation by basin, urban (municipality) population and HPI and HDI. HPI and HDI do not explain much variation in flood impact and their regression coefficients are not significant, but their coefficients are of the right sign (higher HPI leads to higher damage, and the converse with HDI). With more area specific data it is possible that stronger relationships could be found. However, the important point of the model is that a hypothetical explanation for the ranking of flood magnitude can be presented, even with the coarsest of data derived from District level statistics.

G. Conclusions

250. A simple ranking of both approaches to classifying modeled basin areas was carried out, by combining the names of basins which ranked highly in frequency and magnitude of flood damage (Table 20) and socio-economic characteristics (Table 22), based on the ranking frequency of occurrence. The basin ranks were summed and re-ranked. Table 24 shows the result.

251. The final selection of basins for pre-feasibility study was carried out in consultation with the Client, taking into consideration:

Complications with flood management works carried on downstream in India

Existing or planned DWIDP projects

The importance of an equitable distribution of projects between Regions

The presence of important regional centres within basins

Access as some Districts within the study basins had political and security difficulties during the study period that would prejudice project implementation.

252. Table 24 summarises these issues and highlights in yellow the six candidate basins:

Biring

Mawa Ratuwa

Lakhandehi

East Rapti

West Rapti

Mohana.

253. Biring in the New Province 1 has a high house quality index and a reasonable incidence of flooding with widespread damage reported in recent years.

254. Mawa Ratuwa in the New Province 1 seems an excellent choice, particularly given the incidence of frequent high impact floods in Damak municipality.

255. Lakhandehi in the New Province 2 has a high poverty index, poor quality housing and high flood impact. There may be some security concerns and this will need to be reviewed.

256. East Rapti in the New Province 3 has a high population and population growth rate,


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high degree of urbanization, high poverty index and flooding issues.

257. West Rapti in New Province 5 contains the largest fertile valley in Nepal. It appears to have a history of frequent but low intensity floods and is mid-ranked for flood impact. Flood damage is mostly from affected housing and agriculture. Avoided losses are unlikely to justify a high level of investment, particularly if the areas to protect are fragmented and small.

258. Mohana in New Province 7 has a history of flood damage. The river runs close to the regional centre of Dhangadi which is highly populated and has experienced rapid growth in recent years.

259. These six candidate basins were agreed with DWIDP and WRPPF and a letter of confirmation to proceed on this basis was received from the Client in October 2015.


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Table 24: Ranking of Basin Model Areas and Selection of Basins for Pre-feasibility Study

Basin rank is the sum of rankings of population, house quality, HPI, urbanization and flood impact


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VIII. IDENTIFICATION OF PRIORITY PROJECTS

A. Introduction

260. The results of the hydraulic modelling and flood inundation, maximum velocity, flood hazard and flood risk maps produced as part of this study provided useful insight into the mechanism of flooding in the various basins and vulnerable areas. This enabled the study team to bring together the findings from the field visits which were undertaken by the modellers to verify the modelling results and in particular the extent of flood inundation.

261. The insight gained through field visits undertaken by the social team also provided useful information on vulnerable communities in the basin, views on the type of interventions being requested, their impact on the community as well as constraints that would inhibit flood intervention measures. These insights provided the basis for the identification of flood mitigation measures and the required social safeguard measures (ADB, 2009).

262. It was clear from the field visits that there was a need for flood protection as the residents in all the priority basins spoke of the constant fear during the wet season and the short lead times before their areas were inundated. The multiple flood flow paths also suggested that the solutions to the flooding issues would not necessarily be localized but would need to be looked at in a comprehensive manner as restricting one flow path may precipitate flooding elsewhere.

B. Type of Interventions

263. A number of flood mitigation options were identified and considered in discussions with DWIDP for each basin based on the modelling results and maps produced and these were checked in an iterative manner using the models.

264. Flood embankments were considered and suitable locations were identified which balanced the tradeoff between costs and maximizing the area and assets that would be protected. A typical flood embankment drawing is shown in

265. Check dams which are mainly used as sediment traps and for reducing river bed and bank erosion were considered in consultation with DWIDP. The practice by DWIDP is to locate two or three low cost check dams on sediment producing streams. These are generally located in the upper reaches of the basin where the topography and geology are suitable.

266. These structures were located on the basis of examination of topographical maps, google maps, DWIDP experience and local knowledge. Their effect however, cannot be modelled as data on sediment load is not available and the hydraulic model is not a sediment transport model.

267. Bank protection works (Spurs) were considered in consultation with DWIDP. They are provided to prevent bank erosion in susceptible areas, particularly in the vicinity of settlements.

268. Anti-flood sluices – were considered in consultation with DWIDP to prevent flooding from the main river and suitable locations were identified.

269. Early warning systems which are non-structural measures were also considered to be provided in each of the priority basins. These would serve to provide advance warning during floods which would enable the residents to save their


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valuables and move to safer areas.

270. Flood shelters - provision has been made to provide flood shelters in each basin.

271. Training and Capacity building measures - provision has been made for training and capacity building in early warning systems and for information dissemination.

272. River planting - A provision has been made to allow for river planting and other bio-engineering options as per the DWIDP practice.

273. A factor which was also considered was the willingness of the affected people to be participants in the process. Many people had expressed a view that they would be willing to sacrifice some land for the construction of the embankment for the longer term benefits in the same manner that the People’s project carried out by DWIDP functions even though much of their work is for river bank protection rather than constructing flood embankments.

274. All structures were designed using the standard DWIDP design methodology and costs were estimated using local rates applicable to each basin. Details of the identified projects, quantities and rates and typical drawings are provided in the Concept Notes in Appendix E to Appendix J These consist of a package of measures including structural and non-structural measures which maximize the area protected for the minimum length of embankment and other interventions, whilst maximizing the assets protected.

275. Typical drawings for flood mitigation structures are shown in Figure 23 to Figure 27.


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Figure 23: Typical Flood Embankment


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Figure 24: Typical Solid Spur


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Figure 25: Typical Sloping Spur


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Figure 26: Typical Anti-Flood Sluice


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Figure 27: Typical Check Dam

WRPPF-Package 3: Flood Hazard Mapping & Preliminary Preparation of Risk Management Final Report May 2016

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IX. COST BENEFIT ANALYSIS

A. Methodology of the CBA

276. A general model was prepared for the calculation of incremental avoided losses and incurred benefits between the without-project situation and the future with-project (with flood management) situation for all six Priority Basins. The model estimates avoided losses (the difference between losses experienced in the without and with-project situations) which are classified as:

Direct losses: experienced as an immediate impact of a flood and dependent on flood magnitude and periodicity, for example damage or destruction of property and crops Indirect losses: losses experienced as a result of changing markets and technologies as a result of a history of periodic flooding, for example changes in food prices and flood prevention strategies.

277. Being directly proportional to a flood of specific magnitude and periodicity, direct losses are calculated in terms of an Annual Probability of Loss (APL). This is the sum of expected damages caused by a probability set of floods: damage resulting from each flood is multiplied by the probability of occurrence of each flood.

278. The model also calculates tangible benefits. Tangible benefits are not dependent on flood probability and can be inserted in the CBA without adjustment for likelihood of occurrence. They are classified as:

Indirect benefits: experienced as a result of changing markets and technologies as a result of flood management, for example improvements in crop technologies as a result of flood protection Direct benefits: resulting from initiatives within the flood management project itself, such as riverside plantations

279. The model also makes an attempt to calculate intangible losses and benefits, which include:

Death and health impacts Psychological effects (e.g. fears of loss of life, anxiety about moving to temporary accommodation) Loss of irreplaceable items and items of sentimental value

280. The CBA includes estimates of the probability of mortality and morbidity by basin, based on historical flood records. Human life is specifically not valued (due to the methodological and philosophical issues explained in the reports) but direct costs have been added for funerals and hospital treatment. Psychological stress is a key intangible that is not included in the CBA. Such intangible costs can be assessed using contingent valuation; that is asking respondents how much they are “willing to pay to avoid”. This would capture the impact of psychological effects but the valuation would also include other cost components such as fear of loss of livelihood – the cost implications of loss of livelihood can and has been calculated as part of direct costs.

281. Avoided losses and benefits are estimated under the headings of infrastructure, agriculture, human mortality, livestock mortality and a miscellaneous heading entitled “prevention and coping mechanisms”. Since the Draft Final Report, additional benefits have been compiled from avoided loss of eroded land and riverbank plantation. Table 25 below shows the type of benefit estimated and gives a very brief description of the


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method of estimation. The estimated annual benefits for Year 12 of project life are given for each project: note that annual benefits vary depending on projections of future economic change in the basin.

282. Note the Consultants’ benefit estimate does not make use of expected increases in land values with-project. According to a well-known and applied publication in the UK:

“Flood risk reduction may result in enhanced land values in both urban and rural areas. In each case this may result from the same land use becoming more profitable, as a result of the reduced hazard, measured either in terms of the greater production obtained or in terms of the rent that the land commands. Alternatively a new land use may be possible – in the urban context perhaps allowing residential development where only recreational land existed before, and in agricultural areas allowing cereal or root crops where previously rough grazing predominated.

In the urban context, the Treasury has not allowed as a benefit of flood risk management the enhanced value of land which could be converted to more productive economic activity (i.e. the move from recreational land to residential development indicated above). This is because to do so would provide a subsidy to that development, through flood risk management being provided by the state, based on counting the private gain to the developer from the protection thus afforded.”

Flood and Coastal Erosion Risk Management: A Manual for Economic Appraisal, 2014 (Routlidge), Edmund Penning-Rowsell, Sally Priest, Dennis Parker, Joe Morris, Sylvia Tunstall, Christophe Viavattene, John Chatterton, Damon Owen: Chapter 3 Flood Risk Management Theory and Practice, Section 3.2.4 Land Enhancement with Flood Risk Management

283. Following this reasoning, the Consultants prefer to calculate and use as a benefit the annual change in productivity of land (reduction or elimination of direct losses plus indirect benefit of economic productivity increases) as a result of a publicly funded flood management. This is a major component of land price but excludes any speculative value accrued from land improvement, the costs of which have been assumed by government.


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Table 25: Tabulation of Estimated Economic Benefits with Project from Future Floods with Climate Change


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284. The model incorporates the expected costs of the proposed flood management project. Costs changed radically between draft final and final report (due to the addition of spurs, check dams, bio-engineering works and non-structural measures) and are reproduced in financial prices in Table 26 below. The total cost estimate is NPR 5.82 billion (US$ 54.95 million).

285. These financial prices have been converted to economic values. Allowing for the removal of taxes, price contingencies and costs of land acquisition (transfer costs), adjusting imported goods by the Standard Conversion Factor to allow for the premium attached to foreign exchange, and adjusting unskilled labour by the shadow wage rate, gives an economic conversion factor of 0.86 for each project. The total economic cost is estimated as NPR 5.01 billion.


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Table 26: Tabulation of Estimated Costs by Project, NPR m

No. Description of work Mawa Ratuwa East Rapti Biring Lakhandehi West Rapti Mohana Total

A Preliminary works 1 Survey and investigation works for the

preparation detailed project report 2,500,000 1,500,000 2,500,000 2,000,000 2,500,000 1,000,000 12,000,000

Sub-total 2,500,000 1,500,000 2,500,000 2,000,000 2,500,000 1,000,000 12,000,000

B Structural Measures -

1 Land acquisition and Resettlement 33,750,000 5,050,000 33,750,000 20,500,000 49,200,000 6,750,000 149,000,000

2 Embankment/Revetment 518,111,414 160,632,028 489,565,415 282,853,503 799,852,921 120,373,446 2,371,388,727

3 Solid Spur 93,415,025 99,648,226 61,197,277 60,295,631 329,821,819 21,866,010 666,243,988

4 Slopping Spur 59,261,955 60,895,277 43,233,909 83,692,109 247,083,250

Sub-total 704,538,394 265,330,254 645,407,969 406,883,043 1,262,566,849 148,989,456 3,433,715,965

C Catchment Area Treatment Works -

1 Check Dam 258,135,448 156,079,747 223,150,687 209,179,406 301,445,260 115,679,100 1,263,669,648

2 Bio-engineering works 20,126,652 7,808,408 18,349,739 11,591,491 36,401,005 4,267,183 98,544,478

Sub-total 278,262,100 163,888,155 241,500,426 220,770,897 337,846,265 119,946,283 1,362,214,126

D Non-Structural Measures -

1 Establishment Early Warning system 2,500,000 2,500,000 2,500,000 2,500,000 5,000,000 2,500,000 17,500,000

2 Training and Capacity building 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 6,000,000

Sub-total 3,500,000 3,500,000 3,500,000 3,500,000 6,000,000 3,500,000 23,500,000

E Miscellaneous Works -

1 Establishment of Shelter house 17,500,000 17,500,000 17,500,000 17,500,000 17,500,000 17,500,000 105,000,000

F VAT 13 % of (A+B+C+D+E) 130,819,064 58,723,393 118,353,091 84,585,012 211,433,705 37,821,646 641,735,912

G Contingencies

1 Work charge staff contingencies @ 2.5 % of (A+B+C+D+E)

25,157,512 11,292,960 22,760,210 16,266,349 40,660,328 7,273,393 123,410,752

2 Other minor expenses @ 2.5 % of (A+B+C+D+E)

25,157,512 11,292,960 22,760,210 16,266,349 40,660,328 7,273,393 123,410,752

Sub-total 50,315,025 22,585,920 45,520,420 32,532,697 81,320,656 14,546,787 246,821,505

Total 1,187,434,583 533,027,723 1,074,281,906 767,771,649 1,919,167,475 343,304,172 5,824,987,507

Project costs in Economic Values 1,021,193,741 458,403,841 923,882,439 660,283,618 1,650,484,028 295,241,588 5,009,489,256


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286. Maintenance costs are estimated as 1% per annum of investment costs in financial prices. A similar conversion factor is used to estimate the economic cost of maintenance.

287. The incremental benefit of the flood management project is the difference between avoided losses at basin level in the without and with-project situations, plus indirect and direct benefits obtained as a result of the proposed project. Avoided losses are weighted by the probability of their future occurrence (APL) and benefits independent of flood events are scheduled with reference to the flood management project time frame. Then, by subtracting the investment and operational costs of the flood management project from its expected incremental benefits, a benefit stream is derived. This is analyzed to obtain the usual project performance indicators of net present value (NPV), internal rate of return (IRR) and benefit-cost ratio (BCR). Sensitivity analysis has been carried out.

288. The expected benefit from saving of human life and injury as a result of the flood management project is also calculated. The inclusion of Early Warning Systems in each project is expected to make substantial reductions in expected future mortality and morbidity rates at basin level (i.e. both within and outside the with-project flood protected area). The numeraire used is expected numbers of casualties saved during the duration of the project. There is no need to express this in monetary terms.

289. The data required to mobilize the model are the hydrological characteristics (physical extent and aggression as defined by the Flood Hazard Rating, see below) of floods of different probabilities (1 year in 2 (50% probability), 1 year in 5 (20%), 1 year in 10 (10%), 1 year in 25 (4%), 1 year in 50 (2%) and 1 year in 100 (1%)) and infrastructure and land use data within each of the “flood envelopes” impacted by these floods of defined probability with climate change, both without and with-project. The impact of floods with intermediate probabilities is interpolated.

290. The Flood Hazard Rating (FHR) is the product of the predicted depth, velocity and debris content of floods within flood envelopes. Duration is not included in the rating. This does not matter because flood duration is usually less than one day in the Priority Basin areas. The FHR is a quantitative, continuous variable. The higher the rating, the greater the risks to human life, and also of flood damage to property and crops. The damage impact as determined by the FHR is weighted in two ways. The first is required to estimate the flood impact on housing, so the % of houses located in areas of low, moderate, significant and extreme FHR areas is multiplied by the FHR of each class to obtain the FHR applicable to housing in each flood envelope. The weighted FHR for agricultural areas follows a similar procedure, but weighting by the area in each FHR class. It is attempted to preserve the dimension of the FHR by giving the flowing values to each rank:

Low: FHR=<0.75 Moderate: FHR=1.25 Significant: FHR=2.0 Extreme: FHR=>3.0

291. The weighted values are then used in Lookup Tables to give an estimate of damage to house (by four different types of house and associated public infrastructure) and yield reduction of crops. The Lookup Tables are available in the CBA reports for each site in the concept notes in Appendix E to Appendix J: the Lookup Table for crop yield reduction varies between subproject area depending on monthly crop stage and the likelihood of flooding.

292. The relationship between flood magnitude and periodicity and damage caused is obtained from the historical flood records for each basin, collected and collated by MOHA and DWIDP. It was challenging to identify the periodicity of each recorded flood and establish a relationship between flood return period and reported damage. Multi-variable


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analysis was used to estimate statistically significant coefficients that could be used to predict damage from future floods. The concept notes in Appendix E to Appendix J provide more information on this technique.

293. The model must be run with the hydrological characteristics of expected future floods taking into account climate change. With climate change, the estimated flood envelope of a flood of defined probability is larger and the flood is more aggressive. More significant however is the increase in probability of floods of specified magnitude. Bearing in mind that APL is calculated by multiplying damage by probability it is clear that expected future damage from a probability set of floods will increase. The benefits of flood management projects will considerably increase when allowing for the impact of climate change.

294. The model must also be run in financial and economic prices. Therefore for each Priority Basin a financial and economic valuation of the proposed flood management project is calculated for the with-climate change scenario and the project indicators are calculated in economic and financial prices. The derivation of economic prices is available in the CBA reports for each subproject.

295. The results of the CBA analysis are given in the section below.

B. Without-project Population and Land Use in Priority Basins

296. The available data give some idea of land use, and more importantly the area available for expansion and intensification of present land use in the Priority Basins which will represent increased economic activity. Flood management projects can claim benefit from saving not only present assets and lives but also assets and lives that may be established in the future. These future assets are the result of “normal” economic and population growth, which is substantial in all the Priority Basins. But in addition and with-project, incremental growth is expected as a result of increased confidence in flood security, improved infrastructure, better marketing, etc.

297. The total population in the 1:100 historical flood envelope of all Priority Basins is about 106,000 persons in 22,000 households (average household size 4.86). See Table 27. Of these, 84% might be assumed to have rurally based livelihoods. Mawa Ratuwa and Mohana Priority Basins have significant urban populations within the 1 year in 100 CC flood envelope: 62% of estimated housing in Damak Municipality and 28% in Dhangadhi Municipality respectively. Bharatpur Municipality is associated with VDC in East Rapti but according to the 2010 Population and Housing Census most of the population lives outside the flood plain.


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Table 27: Urban and Rural Population of Priority Basins

Historical flood envelopes

2 5 10 25 50 100

50% 20% 10% 4% 2% 1%

Urban population

West Rapti - - - - - -

Mawa Ratuwa 12,109 12,604 12,281 12,281 13,043 13,344

Lakhandehi - - - - - -

Biring - - - - - -

Mohana 2,289 2,827 2,780 2,970 3,101 3,227

East Rapti 24 45 49 53 55 57

Rural population

West Rapti 9,433 22,914 23,754 24,859 25,471 26,078

Mawa Ratuwa 6,746 6,923 7,407 7,407 7,852 8,010

Lakhandehi 13,980 16,772 17,606 19,065 20,131 20,619

Biring 3,451 6,548 6,891 7,629 8,078 8,440

Mohana 6,476 6,837 7,460 7,905 8,174 8,414

East Rapti 4,021 13,416 14,813 16,145 16,755 17,634 298. Dividing the Priority Basin population by house class, 66,000 people (62%) live in house classes 3 and 4 and can therefore be considered to be relatively poor. According to the Population and Housing Census 2010, the total population of all VDC and municipalities associated with 25 basin areas modeled by this Project was 1.7 million households (about 8.7 million people) in 2010. Of these, only 51% lived in class 3 and 4 housing. The statistics suggest a marginalization of relatively poorer households in the flood plain areas.

299. The agricultural area is known by envelope, and the potential arable area can be obtained by subtracting, river, bare and built up areas, see Table 28.


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Table 28: Agricultural and Non-arable Areas of Priority Basins (ha)


2 5 10 25 50 100

50% 20% 10% 4% 2% 1%

Total ha in Envelope

West Rapti 7,260 14,970 15,760 16,643 17,186 17,637

Mawa Ratuwa 3,529 3,840 4,013 4,215 4,346 4,456

Lakhandehi 3,075 3,836 4,313 4,736 4,976 5,172

Biring 1,307 2,809 2,992 3,239 3,408 3,578

Mohana 1,202 1,340 1,419 1,507 1,565 1,619

East Rapti 5,189 9,697 10,178 10,586 10,819 11,001

Agricultural ha in envelope

West Rapti 2,449 7,559 8,124 8,764 9,158 9,483

Mawa Ratuwa 2,671 2,943 3,096 3,272 3,386 3,482

Lakhandehi 2,830 3,519 3,963 4,339 4,556 4,732

Biring 735 2,063 2,210 2,407 2,545 2,688

Mohana 681 775 835 897 936 976

East Rapti 2,629 5,565 5,860 6,111 6,253 6,364

Non arable ha in envelope

West Rapti 4,568 6,876 7,041 7,204 7,294 7,364

Mawa Ratuwa 855 893 913 939 956 970

Lakhandehi 217 278 308 350 372 390

Biring 568 742 778 829 860 887

Mohana 276 298 308 319 328 336

East Rapti 2,239 2,803 2,884 2,948 2,982 3,009 300. From this the percentage of the arable area that is also classified as agricultural can be derived, see Table 29. Only in Mohana and East Rapti is there a significant possibility of expanding the agricultural area. In fact the CBA of the projects in Priority Basins assume that all agricultural benefits come from crop intensification, not from expansion of cropped area. We have however assumed that the “agricultural” area has 100% cropping intensity during the flood risk period. This is unlikely to be true (and so benefits from avoided crop loss are over-estimated), but without field surveys the cropping intensity cannot be estimated. However, the figures do suggest that if there is further in-migration to the Priority Basins then farms will get smaller. There is no evidence for more “unused” arable land in areas of greater flood risk – the proportions are almost constant between the envelopes.

Table 29: Agricultural Area as % of Arable Area


2 5 10 25 50 100

50% 20% 10% 4% 2% 1%

Agricultural area as % of arable area

West Rapti 91% 93% 93% 93% 93% 92%

Mawa Ratuwa 100% 100% 100% 100% 100% 100%

Lakhandehi 99% 99% 99% 99% 99% 99%

Biring 100% 100% 100% 100% 100% 100%

Mohana 74% 74% 75% 76% 76% 76%

East Rapti 89% 81% 80% 80% 80% 80%


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301. However, “farm” areas (ha per rural house) remain quite large, see Table 30, except in Mohana, though according to the Table 29 above there is room for expanding the agricultural area in Mohana: perhaps there are marketing difficulties. In all cases except East Rapti the area per house gets larger as the return period decreases. This suggests that housing is clustered in the envelopes where flood return period is shorter. GIS measurements made in 1992 confirm this, though the mechanism behind the reverse trend at East Rapti is not known. A reasonable question is why should basin settlement be clustered in areas that flood frequently rather than taking a risk of infrequent but aggressive flooding in flood envelopes of low frequency? The historical flood damage record suggests that the proportion of houses actually damaged or destroyed within a high frequency flood envelope is relatively low, compared with a low frequency flood envelope which, on the rare occasions it does flood, damages or destroys a much higher proportion.

Table 30: Hectares per Rural House


2 5 10 25 50 100

50% 20% 10% 4% 2% 1%

Ha per rural house

West Rapti 1.33 1.69 1.75 1.80 1.84 1.86

Mawa Ratuwa 1.71 1.84 1.81 1.91 1.86 1.88

Lakhandehi 1.17 1.21 1.30 1.32 1.31 1.33

Biring 0.94 1.38 1.41 1.38 1.38 1.40

Mohana 0.54 0.58 0.57 0.58 0.58 0.59

East Rapti 2.91 1.85 1.76 1.68 1.66 1.61 302. Benefits have been assumed from providing flood protection for a doubling of economic activity within the flood-managed area over the 25-year life of the project. This assumption is examined for each Priority Basin. It looks as though the area use of land in most of the priority basins (except Mohana) is just about complete, so agricultural expansion will have to come from intensification of land use (though it would be surprising if the cropping intensity on agricultural land were 100% so there is considerable potential for expansion there). Three of the basins have municipal areas associated with them and it could be suggested that this urban presence would be a significant driver for economic growth within the flood plain. All in all, and taking into account the historic rates of expansion of economic activity as represented by population and agriculture (approaching 5% growth per annum) a doubling of economic activity over 25 years of project life is considered reasonable.


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C. Description of With-project Interventions

303. Table 31 shows the investment cost of each subproject and the length of embankment proposed as part of the package of measures. In financial prices the average cost of flood management is about US$ 7,143 per protected ha. The ratio of proposed embankment length to the area protected is about 1 km to 83 ha. An Early Warning System will be provided for each basin (assumed to be available to both protected and un-protected areas) together with Flood Shelter accommodation (see Table 26).

304. Also given in Table 31 is the total area and the agricultural area that will be protected, and the number of houses within the protected area. Four of the subprojects propose to protect significant proportions of the priority basins. The subprojects in East and West Rapti protect only small proportions of these (very large) basins. Where possible the areas selected for protection are in a municipal ward, have relatively higher housing density and contain a higher proportion of agricultural land than in the basin as a whole.

Table 31: Proposed With-project Interventions in Priority Basins

Investment cost, US$

Length of embankment designed, km

Total area of flood

protection at 1:50

year flood with

climate change, ha

Agricultural area of flood protection at

1:50 year flood with climate

change, ha

With-project

number of houses

protected in 2010

from 1:50 flood with climate change

% of basin area

protected at 1:50

year flood with

climate change

% of basin agricultural

area protected at

1:50 year flood with climate change

% of basin houses

protected at 1:50

year flood with

climate change

Percentage of houses protected that are urban

West Rapti 18,105,354 21.59 2,092 1902 2,500 12.1% 12% 50%

Mawa Ratuwa 11,202,213 20.99 1,502 1,286 1,258 34% 37% 26% 59%

Lakhandehi 7,243,129 14.70 2,302 2,284 1,393 45% 49% 40%

Biring 10,134,735 23.22 1,221 1,156 368 35% 45% 20%

Mohana 3,238,719 6.68 265 238 575 16% 24% 26% 69%

East Rapti 5,028,563 5.8 312 267 250 2.82% 4.2% 7%

Total 54,952,712 92.98 7,693 7,133 6,344

305. Notable in respect of project costs is the difference between the financial price and the economic valuation, which deducts contingencies, taxes and transfer costs. This adjustment tends to make all projects rather more attractive in economic terms (see Table 26). Embankment design is to resist 1:50 year floods with climate change.

306. The composition of benefits is described in Table 25. Overall indirect benefits account for 32% of total benefits. A high proportion of indirect benefits is expected in relatively poor project areas: existing assets are low (so direct damage from floods is also low) but there is good potential for asset accumulation with-project. Future growth of economic activity in the protected areas (both as a projection of existing economic activity and incremental economic activity as a result of the security given by flood protection) is accounted as a project benefit, though it is heavily discounted by using a discount rate of 10%. In the case of a relatively disadvantaged flood plain population it may be that this discount rate is too high, see paragraph 322 to 324 below.

307. Valuing benefits in economic prices tends to reduce benefits where financial values include taxes and a large local labour component, but costs valued in economic prices reduce more. Thus the economic returns of each project are consistently better than the financial.

308. The total financial cost of the six flood management projects proposed is about US$ 55 million.


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D. Results of the CBA in Priority Basins

309. The subproject reports contain the details of the CBA for each subproject identified. The main output is the calculation of standard financial and economic project indicators (Net Present Value (NPV), Internal Rate of Return (IRR) and Benefit:Cost Ratio (BCR)). An estimate was made of casualties expected in without and with-project, enabling an approximation of saved casualties that can be attributed to the proposed projects. A sensitivity analysis was carried out on the results, which included the calculation of switching values in economic prices using historical flood data. Switching values are the change required in costs or benefits to return an NPV of zero.

310. NPV is the most reliable of project financial and economic indicators. It represents the present value of the total net income from the project. All other things being equal (project risk, mutually exclusive projects, availability of funding etc.) the project with the largest economic NPV would be chosen for implementation by a national funding agency. Table 32 gives the calculated NPV using 10% discount rate in financial and economic prices using expected future flood data with climate change. Using economic prices, Lakhandehi, Mohana, West Rapti, and Mawa Ratuwa are ranked first, second, third and fourth respectively. The economic NPV is positive and indicates that all four projects may be implemented and are likely to make a positive contribution to the national economy.

311. East Rapti and Biring do not have positive economic NPV. The cost per ha of the East Rapti project is the highest of all (US$ 16,117 per ha), the East Rapti area is small (312 ha) and the number of houses protected is also small (264). Biring has lower investment costs per unit area but the problem at Biring is the very small number of houses protected by the proposed works (368) compared with the size of the area protected (1,221 ha). As a result the avoided direct damage to housing and infrastructure at Biring is also low: only 24% of total benefits estimated for Year 12 compared with about 55% for the other projects.

312. The importance of Government intervention in flood management in the project areas is made very clear by these results. With the exception of Mohana and Lakhandehi, none of the projects have a positive financial NPV: in general flood management will not yield a financial return but Government may be reasonably confident that the economy as a whole will, for those projects with positive NPV, benefit from their implementation.

Table 32: Financial Indicators for Projects in Priority Basins (NPR m)

Net Present Value, NPR m Internal Rate of Return Benefit Cost Ratio

Financial With

Climate Change

Economic With

Climate Change

Financial Rank With

Climate Change

Economic Rank With

Climate Change

Financial With

Climate Change

Economic With

Climate Change

Financial Rank With

Climate Change

Economic Rank With

Climate Change

Financial With

Climate Change

Economic With

Climate Change

Financial Rank With

Climate Change

Economic Rank With

Climate Change

West Rapti -165.22 83.22 5 3 8.8% 10.7% 3 3 0.91 1.05 3 3

Mawa Ratuwa -130.85 44.69 3 4 8.6% 10.5% 4 4 0.88 1.05 4 4

Lakhandehi 279.24 400.66 1 1 14.0% 16.3% 2 2 1.40 1.65 2 2

Biring -595.23 -452.81 6 6 1.3% 2.7% 6 6 0.40 0.47 6 6

Mohana 270.58 321.68 2 2 17.9% 20.6% 1 1 1.80 2.09 1 1

East Rapti -152.25 -152.25 4 5 6.2% 7.9% 5 5 0.71 0.83 5 5

313. The values estimated for NPV are reflected in the other important indicators of IRR and BCR; see Table 32. IRR represents the rate of interest that drives the value of the NPV to zero, so if the discount rate is 10% and IRR is 10% then the NPV must equal zero. If IRR is greater than the discount rate then this means the project is achieving a rate of return higher than the required rate. Only East Rapti and Biring fail to meet the criterion.


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314. Benefit: Cost Ratio is the ratio between the discounted (using the adopted discount rate) stream of costs and the stream of benefits over the project life. If the BCR is greater than unity, this implies the present value of future benefits is greater than the present cost of implementation. Only East Rapti and Biring fail to meet the criterion.

315. Financial and economic indicators are not the whole story in evaluating flood management projects. Reduction in the casualties (fatalities are mostly reported but morbidity is often not and psychological stress never) is an important benefit of flood management projects that is not amenable to financial or economic valuation. Table 33 below shows the expected casualties saved over the life of each proposed project, assuming that the EWS in each basin benefits the entire basin population and as a result reduces mortality and morbidity from floods by half the expected rate without project.

Table 33: Saved Casualties Attributed to Proposed Projects in Priority Basins

Priority basin

Total dead, missing and

injured saved over project life

(25 years) Rank

West Rapti 31.55 5

Mawa Ratuwa 66.59 1

Lakhandehi 41.11 3

Biring 14.97 6

Mohana 47.60 2

East Rapti 35.43 4

Total 237.25

316. These are significant benefits, especially bearing in mind that morbidity is probably under-reported. Note the higher values of casualties saved are associated with basins with high populations; that is the reason why Biring performs poorly by this measure as well as by financial and economic indicators.

E. Sensitivity Analysis

317. Risks attached to implementation and operation are as important as the indicators calculated. Table 34 below gives the switching values which show the percentage change required in costs and benefits to drive the economic NPV calculated with future floods expected under climate change conditions to zero. All positively valued projects are sensitive to cost increases except Lakhandehi and Mohana, which would need a large cost increases to drive NPV to zero. Quite small project cost increases in West Rapti and Mawa Ratuwa would have the same effect. East Rapti, with negative NPV, would require a cost reduction of 82% to raise the NPV to zero. Biring would require a 42% reduction, which is considerable. Costs of embankment and other structural measures are unlikely to depart substantially from their estimates, so in this respect all projects except Lakhandehi and Mohana are “risky”.


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Table 34: Switching Values for Projects in Priority Basins

Costs Benefits Rank in Costs Rank in Benefits

West Rapti 106% 95% 3 4

Mawa Ratuwa 105% 96% 4 3

Lakhandehi 172% 61% 2 5

Biring 42% 211% 6 1

Mohana 220% 48% 1 6

East Rapti 82% 120% 5 2 318. A similar picture is gained from switching values for benefits. Quite small decreases in estimated benefits for West Rapti and Mawa Ratuwa would drive NPV down to zero. Lakhandehi and Mohana are robust: benefits would have to fall to 61% and 48% respectively of their estimated value to return an NPV of zero.

319. It is quite clear that West Rapti, Mawa Ratuwa and East Rapti as costed and with benefits as estimated are not particularly robust. Biring is extremely insensitive to reaching a satisfactory NPV. Lakhandehi and Mohana appear sound: a negative NPV would appear unlikely for either. Note though that the financial and economic indicators calculated by the Consultants are prefeasibility estimates. Note also that the historic flood record, on which future direct avoided losses are based, may be incomplete in some aspects. Damage reported for relatively frequent floods is likely to be under-reported. Adjusting for this probability (if it could be done) would drive benefits up considerably.

320. The sensitivity analysis in each of the CBA reports in the concept notes in Appendix E to Appendix J probe more deeply into the causes of financial and economic risk to implementation and performance of the proposed projects. Most of the risks are related to benefit estimation. The prefeasibility investigations required value estimates of housing, agriculture and livestock at basin level, on which were based historical flood damage estimates. It was necessary to calculate damage based on to the estimated value of assets during the recorded year of damaging flood events and inflate to current prices using the appropriate price index. The flood events themselves had to be tied to a return period using recorded basin peak discharges for the year in question. Neither the basin asset inventory nor the flood damage records are accurate or complete. This introduces uncertainty into the benefit estimates, which would be considerably reduced by intensifying data collection at feasibility level. Simple cross-section surveys across the flood plain (with an increased sample density in the area to be protected) to establish present house numbers can easily be carried out, together with a building typology based on resistance to floods of different return periods. The present housing inventory could then be compared with known 1992 house numbers and the intermediate stock during the period 1992-present established with a reasonable degree of accuracy. Then the regression analyses establishing observed house losses (from the MoHA/DWIDP database) from floods of known return period can be re-run, which would establish affected, damaged and destroyed housing from different flood events more accurately. Similarly, cross-section surveys of agriculture could be carried out to establish the cropped area by flood envelope, the technology used, planting dates and yields. This would substantially improve the loss estimates made at prefeasibility level, which are based simply on gross margins based on the gross agricultural area.

321. It is therefore considered essential that feasibility studies for each proposed project are carried out in order to provide a sound basis for implementation, as well as a stronger basis for monitoring and evaluation of the flood management measures identified.

322. It would be easy to be critical of the projects as analyzed by cost benefit analysis, except for Lakhandehi and Mohana. The remainder returns a low or even negative NPV,


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which implies a small or negative contribution to Nepal’s future growth. But referring to paragraph 315, all projects do very well in saving casualties, particularly Mohana and Mawa Ratuwa, which have large basin populations, which will benefit from EWS and flood shelters.

323. In addition, flood plains hold a larger proportion of poorer households than VDC areas outside flood plain areas. This is statistically demonstrated through an assessment in the relative quality of housing. If improving the quality of life and asset base of the poorest is a priority, than theoretically a lower social discount rate would be acceptable. East Rapti for example would require a discount rate of 8% to improve NPV to zero and make the project acceptable for implementation. Lowering the discount rate would represent a subsidy from the country to a well-defined disadvantaged area which policy makers may well consider desirable. Even Biring, apparently hopeless in terms of returning positive economic indicators, would require a social discount rate of 3% to achieve an NPV of zero. Such a social discount rate in some developed countries would be considered more than acceptable, especially those where bank rates are turning negative. If funding is available, the setting of the social discount rate raises moral and philosophical questions – why should flood protection be denied to one population and not another simply because of differing macro-economic conditions reflected in the discount rate?

324. Another consideration is the importance of asset accumulation. Given that populations on Terai flood plains are mostly rural and relatively poor and avoided direct losses may be in the region of 60% of total benefits, it follows that many flood plains cannot at present support expensive flood management projects. Possibly it would be a better use of scarce resources to spend less on a project with a short life, allow asset accumulation and then re-build later with a more expensive, stronger structure that is properly justified by the assets accumulated. True, the risk of catastrophe from a severe low-frequency flood that breaches a cheap embankment still exists in the early period. This risk would have to be carefully balanced against the benefit of saving money that could be allocated to another equally poor flood plain population. The joint probability of both populations being victims of a severe low-frequency flood in the short-term future would be low.


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X. PRIORITY PROJECTS

A. Introduction

325. Priority projects have been identified in each of the six priority basins of Biring, Mawa Ratuwa, Lakhandehi, East Rapti, West Rapti and Mohana. These consist of a package of structural and non-structural measures designed for a return period of 50 years plus climate change which is the DWIDP standard for protection of rural settlements.

326. Details of the projects and Concept Notes are given in Appendix E to Appendix J and a summary is shown in Table 35.

Table 35: Summary of Key Features of Priority Projects

Investment cost, US$

Benefit-Cost Ratio - Economic

withClimate Change

Total area of flood

protection at 1:50 year flood with climate

change, ha

Agricultural area of flood protection at

1:50 year flood with climate

change, ha

With-project number of

houses protected in 2010 from 1:50 flood

with climate change

Length of embankment designed, km

Solid Spurs nos

Sloping Spurs nos

Check Dams nos

Anti-Flood

Sluices nos

West Rapti 18,105,354 1.05 2,092 1902 2,500 21.59 30 52 17

Mawa Ratuwa 11,202,213 1.05 1,502 1,286 1,258 20.99 64 38 52

Lakhandehi 7,243,129 1.65 2,302 2,284 1,393 14.70 52 17 42

Biring 10,134,735 0.47 1,221 1,156 368 23.22 50 46 45

Mohana 3,238,719 2.09 265 238 575 6.68 22 25

East Rapti 5,028,563 0.83 312 267 250 5.8 36 34

Total 54,952,712 7,693 7,133 6,344 92.98 254 101 250 17

All basins will have provision for Bio-engineering works, Early warning systems, Flood Shelter and Training and Capacity Building

327. The projects in West Rapti, Mawa Ratuwa, Lakhandehi and Mohana have economic benefit-cost ratios for the climate change scenario of greater than 1 with a discount rate of 10%.

328. The benefit-cost ratios for East Rapti and Biring are less than 1 with a discount rate of 10%. However, there are social justifications for proceeding with this package of measures on the grounds of poverty alleviation and safeguarding of lives lost in floods. If improving the quality of life and asset base of the poorest is a priority, than theoretically a lower social discount rate would be acceptable. With a social discount rate of 8% for East Rapti and 3% for Biring, an NPV of zero is achievable, giving a benefit-cost ratio of 1. Lowering the discount rate would represent a subsidy from the country to a well-defined disadvantaged area which policy makers may well consider desirable. Such a social discount rate in some developed countries would be considered more than acceptable, especially those where bank rates are turning negative.

329. The following sections describe the package of measures considered for each priority basin and further details are given in Appendix E to Appendix J.


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B. West Rapti Basin

330. The West Rapti river rises south of a prominent east-west ridgeline midway between the western Dhaulagiri Himalaya and the Mahabharat Range in north western part of Rolpa District. The River descends south from rugged highland of Rolpa District and crosses into Pyuthan. After the confluence with Jhimruk and Madi, the river is called West Rapti River. It flows for a short distance towards the south west and makes a turn and flows due north west parallel to the Churia (Siwalik) range till Shamserganj in Banke district. Here it makes a turn and flows due south east before joining the Ghagra (Karnali) River in India.

331. The catchment area of the study basin is 6500 km2 and the length of main stream channel is 257 km. The major towns along the study reach are: Bhaluban, Sisahniya, Sonpur, Lamahi, Satbariya, Baijapur, Kachanapur, Phattepur, Betahani and Holiya. The study reach covers Deukhuri valley of Dang district and the plains in Banke district.

332. West Rapti River is a perennial river and flooding in this river is characterized as being flashy in the narrow valley and prolonged in the wider plain areas in the downstream reaches. There has been a history of over 60 flood events between 1993 and 2015 of which a significant proportion cause low to moderate damage.

333. In recent years, the river has experienced high sediment loads often aggravated by landslides in the upper catchment.

334. The poverty levels in West Rapti Basin are around average in the flood affected areas with a HPI of 33. West Rapti basin contains the largest fertile valley in Nepal and has a relatively large population living in the plains. The population growth rate is relatively low at 1.99% but greater than the average in the study area.

335. The package of measures proposed in the Biring Basin consist of:

- 21.6 km of flood embankment of which 14.5km are on the left bank and 4.9km on the right bank.

- 30 solid spurs of which 18 are on the left bank and 12 on the right bank.

- 17 anti-flood sluices with flap gates of which 11 are on the left bank and 6 on the right bank.

- 52 check dams

- Bio-engineering works

- Establishment of an early warning system

- Training and capacity building.

- Establishment of a flood shelter.

336. These works are estimated to cost USD 18.1 million with a benefit-cost ratio of 1.05 at a discount rate of 10%. The area that would be protected at a 50 year plus climate change flood level is 2092 ha of which 1902 ha is agricultural land. The number of houses that would be protected is 2500 and the estimated number of casualties saved over the lifetime of the project is 32.

337. The proposed flood mitigation works are shown in Figure 28.


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Figure 28: Proposed Flood Mitigation Measures in West Rapti Basin


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C. Mawa Ratuwa Basin

338. Mawa Ratuwa River lies in the Eastern part of Nepal in Jhapa district and is a tributary of the Mechi River. It originates in the Mahabharat range of eastern Nepal and flows in a southerly direction to the Nepal-Indian border. The total catchment area of the Mawa Ratuwa basin upto the Nepal-Indian border is 381km2.The main tributary of Mawa Ratuwa is Chulachuli. Mawa Ratuwa River flows east of Damak city, which is one of the major cities of eastern Nepal. The topography of the catchment area is steeper in the upper reaches of the basin and very mild in the lower part of the basin in the Terai region.

339. Mawa Ratuwa River is a non-perennial river and the flooding in this river is characterized as being flashy in nature. There has been a history of 58 flood events between 1993 and 2015 of which a significant proportion have caused moderate damage and loss of life.

340. In recent years, the river has experienced high sediment deposition often aggravated by landslides in the upper catchment. This has led to a rise in the bed level over time.

341. The poverty levels in Mawa Ratuwa Basin in the flood affected areas are relatively low with a HPI of 24.4. This may partly be skewed by the residents of Damak. The population growth rate of 1.12% is low.

342. The package of measures proposed in the Mawa Ratuwa Basin consist of:

- 21 km of flood embankment of which 11 km are on the left bank and 10 km on the right bank.


- 38 sloping spurs of which 20 are on the left bank and 18 on the right bank.

- 52 check dams








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Figure 29: Proposed Flood Mitigation Measures in Mawa Ratuwa Basin


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D. Lakhandehi Basin

345. Lakhandehi River lies in the central part of Nepal in Sarlahi district and is a tributary of the Bagmati River. It originates in the Mahabharat range of central Nepal and flows in a southerly direction to the Nepal-Indian border. It has total catchment area upto the Nepal-Indian border of 344km2. Lakhandehi River flows west of Lalbandi city which is one of the growing cities of central Nepal. The topography of the catchment area is steeper in the upper reaches of the basin and very mild in the lower part of the basin in the Terai region.

346. Lakhandehi River is a non-perennial river and the flooding in this river is characterized as being flashy in nature. There has been a history of 69 flood events between 1993 and 2015 of with widespread damage and loss of life, particularly in the downstream part of the basin in the border region.

347. In recent years, the river has experienced high sediment loads often aggravated by landslides in the upper catchment. This has led to a slow rise in the bed level over time.

348. The poverty levels in Lakhandehi Basin in the flood affected areas are amongst the highest in the study area with HPI of 43.8 and the population growth rate of 2.49% is also high.

349. The package of measures proposed in the Lakhandehi Basin consist of:




- 42 check dams








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Figure 30: Proposed Flood Mitigation Measures in Lakhandehi Basin


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E. Biring Basin

352. Biring Basin is located in the central part of Jhapa district in eastern Nepal. It has a total catchment area of 370 km2 at the Nepal-Indian border. The river flows from north to south between Kankai River on the west and the town of Birtamod on the east. Biring River originates in the Siwalik Hills in Ilam district of eastern Nepal. The headwaters of the Biring River are in Ilam district where about 30 % of the catchment area lies. The rest of its catchment area falls in the Terai plain of Jhapa district. The major towns along the study reach are: Khudunabari, Arjundhara, Ghailadubba, Chakchaki, Tangandubba, and Rajgadh.

353. Biring River is a non-perennial river and the flooding in this river is characterized as being flashy in nature. There has been a history of 43 flood events between 1993 and 2015 of which a significant proportion have caused widespread damage and loss of life.

354. In recent years, the river has experienced high sediment deposition often aggravated by landslides in the upper catchment. This has led to a rise in the bed level over time.

355. The poverty levels in Biring Basin better than average with a HPI of 23.8 and the population growth rate of 1.23% is relatively low.

356. The package of measures proposed in the Biring Basin consist of:




- 45 check dams






358. There are social justifications for proceeding with this package of measures on the grounds of poverty alleviation and safeguarding of lives lost in floods. If improving the quality of life and asset base of the poorest is a priority, than theoretically a lower social discount rate would be acceptable. With a discount social rate of 3%, an NPV of zero is achievable, giving a benefit-cost ratio of 1. Lowering the discount rate would represent a subsidy from the country to a well-defined disadvantaged area which policy makers may well consider desirable. Such a social discount rate in some developed countries would be considered more than acceptable, especially those where bank rates are turning negative.



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Figure 31: Proposed Flood Mitigation Measures in Biring Basin


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F. Mohana Basin

360. Mohana River lies in the far western part of Nepal in Kailali district and is a tributary of the Karnali River. It originates in the Mahabharat range of western Nepal and flows in a southerly direction to the Nepal-Indian border. The total catchment area of the Mohana basin upto the Nepal-Indian border is 410 km2. The main tributaries of the Mohana are Godavari, Manahara and Baluwa. Mohana River flows west of the regional centre of Dhangadhi which is located close to the Nepal-Indian border at the downstream end of the basin. The topography of the catchment area is steeper in the upper reaches of the basin and very mild in the lower part of the basin in the Terai region.

361. Mohana River is a non-perennial river and the flooding in this river is characterized as being flashy in nature. There has been a history of 30 flood events between 1993 and 2015 of which a significant proportion have caused moderate damage and loss of life.

362. In recent years, the river has experienced high sediment loads often aggravated by landslides in the upper catchment. This has led to a rise in the bed level over time.

363. The poverty levels in Mohana Basin are about average with a HPI of 28.9 in the study area and the population growth rate of 2.4% is high.

364. The package of measures proposed in the Mohana Basin consist of:



- 25 check dams








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Figure 32: Proposed Flood Mitigation Measures in Mohana Basin


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G. East Rapti Basin

367. The East Rapti River, which originates from the Mahabharat mountain range in Makwanpur district, is one of the major tributaries of the Narayani River. Flowing in a southward direction, it changes to westerly course near Hetauda till it reaches its confluence with the Narayani River in Chitwan. The catchment area of the basin at the confluence with Narayani River is approximately 3108 km2. The main tributaries of the river are: Manahari, Lothar, Riu, Dhongre, Kageri and Kayar. The major towns along the study reach are: Hetauda, Bhandara, Sauraha, Bachhauli and Meghauli. The study reach covers Chitwan and Makwanpur districts with a significant part through the Chitwan National Park.

368. East Rapti River is a perennial river with a number of tributaries. The flooding characteristics are dependent on the location of the site and can range from being considered prone to flash flooding in the upper parts of the catchment to a more delayed but sustained flooding in the lower part of the catchment. There has been a history of 143 flood events between 1993 and 2015 of which a significant proportion have caused widespread damage and loss of life.

369. In recent years, the river has experienced high sediment loads often aggravated by landslides in the upper catchment. The poverty levels in East Rapti Basin in the flood affected areas are below average for the study area with a HPI of 26.5. It has a high basin population combined with a high population growth rate of 2.14%. It also has a significant urban population.

370. However, as large parts of the basin are part of the Chitwan National Park, there are limited opportunities to carry out large scale flood mitigation measures in these areas.

371. The package of measures proposed in the East Rapti Basin consist of:

- 5.8 km of flood embankment on the right bank.

- 36 sloping spurs the right bank.

- 34 check dams






373. There are social justifications for proceeding with this package of measures on the grounds of poverty alleviation and safeguarding of lives lost in floods. If improving the quality of life and asset base of the poorest is a priority, than theoretically a lower social discount rate would be acceptable. With a discount social rate of 8%, an NPV of zero is achievable, giving a benefit-cost ratio of 1. Lowering the discount rate would represent a subsidy from the country to a well-defined disadvantaged area which policy makers may well consider desirable. Such a social discount rate in some developed countries would be considered more than acceptable, especially those where bank rates are turning negative. The proposed flood mitigation works are shown in Figure 33.


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Figure 33: Proposed Flood Mitigation Measures in East Rapti Basin


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XI. SUMMARY AND RECOMMENDATIONS

A. Summary

374. During the study, many activities were carried out and a number of deliverables were produced. These included:

- Processing CARTOSAT_1 satellite imagery in the Terai region covering the hydraulic modelling extent for 25 river basins and generating Digital Elevation Models.

- Analysing rainfall data to identify evidence of Climate Change applicable to the region and generating daily time series of climate change rainfall at representative rainfall stations in each of the 25 river basins.

- Carrying out extreme value analysis of daily rainfall for historic and climate change conditions.

- Developing HEC-HMS hydrological models for 20 ungauged catchments and 5 gauged catchments in the study area and generating inflows for use in the hydraulic models for historic and climate change conditions at various return periods.

- Developing HEC-RAS models for the 25 study basins in the areas of interest and running simulations for various return periods for the historic and climate change scenarios.

- Generating flood inundation maps, maximum velocity maps, flood hazard maps, flood risk maps and greenbelt maps for various return periods for the historic and climate change scenarios.

- Carrying out basin screening using a variety of indicators to identify 6 priority basins for preparation of project concept notes.

- Carrying out socio-economic surveys in the 6 priority basins.

- Identifying flood mitigation measures in the 6 priority basins including structural and non-structural measures.

- Carrying out designs, drawings and estimating the quantities and costs for a package of flood mitigation measures up to a pre-feasibility level.

- Simulating the effects of the “with project” scenarios using the hydraulic models and generating the associated maps.

- Carrying out economic analysis for the package of flood mitigation measures identified.

- Producing concept notes for the 6 priority basins of Biring, Mawa Ratuwa, Lakhandehi, East Rapti, West Rapti and Mohana.

375. The package of measures in West Rapti, Mawa Ratuwa, Lakhandehi and Mohana basins have economic benefit-cost ratios for the climate change conditions of greater than 1 with a discount rate of 10% and should be considered to be taken forward to the feasibility stage.

376. The benefit-cost ratios for East Rapti and Biring basins are less than 1 with a discount rate of 10%. However, there are social justifications for proceeding with this


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package of measures on the grounds of poverty alleviation and safeguarding of lives lost in floods. If improving the quality of life and asset base of the poorest is a priority, than theoretically a lower social discount rate would be acceptable. With a social discount rate of 8% for East Rapti and 3% for Biring, an NPV of zero is achievable, giving a benefit-cost ratio of 1. Lowering the discount rate would represent a subsidy from the country to a well-defined disadvantaged area which policy makers may well consider desirable. Such a social discount rate in some developed countries would be considered more than acceptable, especially those where bank rates are turning negative. Alternatively, some elements of the package of measures may be taken forward to the feasibility stage.

B. Recommendations for further work in the Terai

377. During the course of the study, it has become clear that the Terai region has many challenges and potential for improvement to fulfill it’s full development potential. From a flood mitigation perspective, there are areas where further work may be considered which would help improve the findings and outcomes of this study. Recommendations are therefore made to the concerned agencies to:

- Disseminate the findings of the study to a wider audience and consider sharing the flood maps with other concerned agencies working in the area.

- Acquire additional satellite imagery to improve and infill the gaps in the DEMs produced as part of this study

- Establish gauging stations or atleast water level recorders along the East-West highway or another suitable site in each of the priority basins to give a reference point for early warning systems in the region.

- Establish a flood response plan for the various communities in the priority basins and build upon the experiences gained from such activities.

- Consider extending the studies to cover the remaining basins in the Terai.

- Establish water level recorders close to the border with India to establish and fully understand the boundary conditions prevailing during flooding events and through the monsoon season.

- Liaise with the Indian authorities to establish the operation and control of water impeding structures on the Indian side of the border and re-run the hydraulic models with a new set of boundary conditions to properly asses the effect on the flood levels on Nepali territory.

- Continue to improve and update the hydrological and hydraulic models developed as part of this study as more data becomes available and the flood maps should be revised periodically and disseminated to concerned agencies.

- Establish a centre of excellence for modelling activities in the DWIDP with properly trained, incentivised and committed staff. Training programs which may include on-the-job training as well as formal structured training for such staff should be considered as part of any development project with a commitment to retain the staff for a period of time following any such training activities.

- Consider extending the modelling studies to developing flood forecasting tools to supplement any early warning systems in the region.

- Establish an inventory of all flood mitigation structures constructed or planned which are located on a map with an associated database and can be accessed


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in digital form. This will aid in the holistic planning of the basins and discourage ad hoc local solutions which may have an adverse effect in other parts of the basin.

- Establish a sediment data collection program in problematic basins to provide a basis for targeted solutions.

- Consider monitoring the sand mining activities in the rivers and encouraging miners to concentrate on mining in areas which will establish low flow channels away from the river banks.


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XII. REFERENCES

ADB (2009) Safeguard Policy Statement Babael, M.S., Bhusal, S.P. and Wahid, S.M. (2013) Climate change and water resources

in the Bagmati River Basin, Nepal, Theoretical and Applied Climatology, 115, 2013. DOI: 10.1007/s00704-013-0910-

Central Bureau of Statistics (2014) Population Monograph of Nepal

CBS (2014) Statistical Pocket Book Nepal 2014. Kathmandu: Government of Nepal, National Planning Commission, Central Bureau of Statistics.

Devkota, L.P. and Gyawali, D.R. (2015) Impacts of climate change on hydrological regime

and water resources management of the Koshi River Basin, Nepal, Journal of hydrology: Regional Studies, Vol. 4, part B, 502-515

DWIDP (2016) Water Induced Disaster Management Policy (in Nepali) Government of Nepal, Ministry of Environment (Sep 2010) National Adaptation Program

of Action (NAPA) to Climate Change Government of Nepal (2014) Nepal Human Development Report 2014, Beyond

Geography – Unlocking Human Potential ICIMOD (2013) Case Studies on Flash Flood Risk Management in the Himalayas in

support of specific flash flood policies IGES (2015) Climate change, changing rainfall and increasing water scarcity: An

integrated approach for planning adaptation and building resilience of smallholder subsistence livelihoods in Nepal

Khadka, A.,Devkota, L.P. and Kayashta, R.B. (2015) Impact of Climate Change on the

Snow Hydrology of Koshi River Basin, Journal of Hydrology and Meteorology, Vol. 9

MHP (2011) Nepal Population Report 2011. Kathmandu: Government of Nepal, Minisry of

Health and Population, Population Division. Ministry of Science, Technology and Environment, Government of Nepal (Jan 2013)

Economic Impact Assessment of Climate Change in Key Sectors in Nepal Penning-Rowsell Edmund, Sally Priest, Dennis Parker, Joe Morris, Sylvia Tunstall,

Christophe Viavattene, John Chatterton, Damon Owen (2014), Flood and Coastal Erosion Risk Management: A Manual for Economic Appraisal, Chapter 3 Flood Risk Management Theory and Practice, Section 3.2.4 Land Enhancement with Flood Risk Management (Roulidge)

Shakya, B. (2002) A new approach within hydrometeorological technique for the

estimation of average depth of probable maximum precipitation (PMP) over Nepal. In: Wu, et al. (Eds.), Flood Defence 2002, Science Press, New York Ltd., pp. 599–606.


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Shakya, B. (2013) Review of Flood Hazard and Flood Hazard Mapping in Nepal, Report

for the Department of Irrigation Sharma, R. K., & Acharya, B. R. (2004) Spatial Data Infrastructure for prosporous Nepal.

3rd FIG Regional Conference, Jakarta, Indonesia.

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