ANZDEC Report

Document Produced under Grant

Project Number: 45206 May 2016

Grant 0299-NEP: Water Resources Project Preparatory Facility

Final Report – Volume 4 Appendix E Biring Annexes 4 to 6

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

This document does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents.

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

ANNEX 4: TYPICAL DRAWINGS

Volume 4: Appendix E Lahmeyer International in association with Total Management Services WRPPF-Package 3: Flood Hazard Mapping & Preliminary Preparation of Risk Management Projects ii Final Report May 2016

ANNEX 4

TYPICAL DRAWINGS

LIST OF FIGURES

Figure 1: Typical Flood Embankment ...... 1 Figure 2: Typical Flood Embankment with Protection Works and Launching Apron ...... 2 Figure 3: Typical Solid Spur ...... 3 Figure 4: Typical Sloping Spur ...... 4 Figure 5: Typical Check Dam ...... 5

Figure 1: Typical Flood Embankment

Volume 4: Appendix E Lahmeyer International in association with Total Management Services

WRPPF-Package 3: Flood Hazard Mapping & Preliminary Preparation of Risk Management Projects 2 Final Report May 2016

Figure 2: Typical Flood Embankment with Protection Works and Launching Apron

Volume 4: Appendix E Lahmeyer International in association with Total Management Services

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

Figure 3: Typical Solid Spur

Volume 4: Appendix E Lahmeyer International in association with Total Management Services

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

ANNEX 5: DESIGNS, QUANTITIES AND UNIT RATES

ANNEX 5

DESIGNS, QUANTITIES AND UNIT RATES

LIST OF TABLES

Table 1: Summary of Project Costs ...... 1 Table 2: Summary of Cost of Structures...... 2 Table 3: Summary of Costs – Left Flood Embankment ...... 3 Table 4: Design of Left Flood Embankment ...... 4 Table 5: Summary of Costs – Right Flood Embankment ...... 8 Table 6: Design of Right Flood Embankment ...... 9 Table 7: Summary of Costs – Solid Spurs ...... 12 Table 8: Summary of Costs – Sloping Spurs ...... 13 Table 9: Summary of Costs – Check Dams ...... 14 Table 10: Summary of Unit Rates ...... 15

Table 1: Summary of Project Costs

Name of work: Biring Flood Management Works

Location: Jhapa District

Name of River: Biring

Item Description of work Amount (NPR) Remarks Nos.

A Preliminary works Survey and investigation works for the 1 preparation detailed project report 2500000.00 Sub-total 2500000.00 B Structural Measures 1 Land acquisition and Resettlement 33750000.00 2 Embankment/Revetment 489565414.76 3 Solid Spur 61197277.50 4 Slopping Spur 60895277.48 Sub-total 645407969.74 C Catchment Area Treatment Works 1 Check Dam 223150687.20 2 Bio-engineering works@ 3% of B 18349739.09 Sub-total 241500426.29 D Non-Structural Measures 1 Establishment of Early Warning system 2,500,000.00 2 Training and Capacity building 1000000.00 Sub-total 3500000.00 E Miscellaneous Works 1 Establishment of Shelter house 17,500,000.00 F VAT VAT 13 % of (A+B+C+D+E) 118353091.48 G Contingencies 1 Work charge staff contingencies @ 2.5 % of 22760209.90 2 Other minor expences @ 2.5 % of 22760209.90 Sub-total 45520419.80

Total 1,074,281,907.32 Total in NPR (million) 1074.28

Table 2: Summary of Cost of Structures

Name of work: Biring Flood Management Works

Location: Jhapa District

Name of River: Biring

S.N. Description of work Location Unit Quantity Amount (NPR) Remarks

1 Left Bank Km 11.5 247457285.37 242108129.39 Embankment/Revetment Right Bank Km 11.7 Total Km 23.2 489,565,414.76

Left Bank Nos 26 31822584.30

2 Solid Spur Right Bank Nos 24 29374693.20

61,197,277.50 Total Nos 50

Left Bank Nos 24 31771449.12

Slopping Spur Nos 29123828.36 Right Bank 22 3 Total Nos 46 60,895,277.48

4 Check Dam Nos 45 223150687.20

5 Bio-engineering works LS @3% 18,349,739.09

7 Establishment of Shelter house Sqft 5,000.00 17,500,000.00

8 Establishment of Early Warning System Nos 1.00 2,500,000.00

Table 3: Summary of Costs – Left Flood Embankment

Name of work: Construction of Embankment Location:- Jhapa district Name of River: Biring Amount S.N Description of work Quantity Unit Rate (NPR) Remarks Earthwork excavation in river bed material and filling with the same material compacted in 15 cm layers with optimum water 1 content for embankment construction including spreading & 229757.91 m3 94.76 21,771,174.22 dressing of soil all complete as per design, drawing, and specification. Earthwork excavation in sweet soil (soft clay/silty soil) and filling with the same material compacted in15 cm layers with 2 optimum water content for embankment construction including 99332.22 m3 180.45 17,924,562.16 transportation, spreading & dressing of soil all complete as per design, Earthwork excavation in river bed material for launching 3 39600.00 53.64 2,123,985.60 foundation including disposal. (Machanical) m3 Supply of boulder & filling in Gabion box including placing in 4 position, tying gabion by tightning wire closing from top all 42900.00 2944.66 126,326,072.21 m3 complete . Supply of 10 gauge medium coated G.I. wire and making 5 rectangular gabion boxes for mesh size 100 mm x 100 mm 239785.20 252.36 60,513,072.28 m2 with two way knot including wire cutting, netting etc. complete. Supply & Laying of Geotextile Terram 2000 or Polyfelt Ts. 50 or 6 55000.00 120.14 6,607,759.12 equivalent., Mass per unit area > 150gm/m2 . m2 Grass sodding works including sod cutting, transporting, 7 153694.44 40.28 6,191,503.80 placing in position and water sprinkling with all lead and lift m2

8 Site clearance 1.00 job 150000.00 150,000.00 Temporarty diversion, dewatering work, temporary approach 9 1.00 job 500000.00 500,000.00 road repair/construction Sub Total of (C) Rs. 242108129.39

Table 4: Design of Left Flood Embankment

stn. Quantity Water Top of Ht. of Area= Mean Turfing Quantity Quantity In Min. ch. tpeak Qpeak RL of Base Length of earth level Depth (m) V (m/s) embankment Emb (5h+2h*h) Area Length of Turfing of sweet Remarks HEC- El (m) (hr) (m3/s) GL (m) Width (m) (m) filling (m) (m) (m) (sq.m) (sq.m) (m) (sq m) (cu m) RAS (cu.m) L_EMB1 (540 m) 30732 132.98 136.04 3.06 2.67 8.0 1159.0 137.5 136.00 1.54 11.16 12.44 30600 132.68 135.74 3.06 2.67 8.0 1159.0 137.2 135.00 2.24 13.96 21.24 16.84 133 2239.61 10.02 1332.30 998.65 30200 131.5 134.71 3.21 2.89 8.0 1159.0 136.2 132.75 3.46 18.84 41.24 31.24 292 9121.85 15.47 4518.15 2989.08 30085 131.2 134.41 3.21 2.89 8.0 1159.0 135.9 134.00 1.91 12.64 16.85 29.04 115 3340.14 8.54 982.27 778.64 Sub Total 540 14701.60 6832.72 4766.36 L_EMB2 (445 m)

29345.* 128.97 131.68 2.71 1.47 8.2 1159.0 133.2 131.75 1.43 10.72 11.24 29330.* 128.97 131.68 2.71 1.47 8.2 1159.0 133.2 131.75 1.43 10.72 11.24 11.24 15 168.60 6.39 95.92 85.46 29200 129 131.08 2.08 2.77 8.2 1159.0 132.6 130.50 2.08 13.32 19.05 15.15 130 1969.02 9.30 1209.23 929.61 ` 127.76 130.24 2.48 2.04 8.2 1159.0 131.7 128.50 3.24 17.96 37.20 28.12 200 5624.80 14.49 2897.86 1948.93 28900 127.34 129.82 2.48 2.04 8.2 1159.0 131.3 129.25 2.07 13.28 18.92 28.06 100 2805.75 9.26 925.70 712.85 Sub Total 445 10568.17 5128.71 3676.86 L_EMB3 (330 m)

28835 127.85 129.29 1.44 3.1 8.3 1159.0 130.8 129.00 1.79 12.16 15.36 28800 127.7 129.14 1.44 3.1 8.3 1159.0 130.6 128.00 2.64 15.56 27.14 21.25 35 743.70 11.81 413.21 294.11 28600 125.73 128.39 2.66 2.07 8.3 1159.0 129.9 127.50 2.39 14.56 23.37 25.26 200 5051.34 10.69 2137.62 1568.81 28505 125.33 127.99 2.66 2.07 8.3 1159.0 129.5 128.00 1.49 10.96 11.89 17.63 95 1675.06 6.66 633.01 554.01 Sub Total 330 7470.10 3183.84 2416.92 L_EMB4 (112 m)

28385 125.46 127.26 1.8 3.24 8.3 1159.0 128.8 127.20 1.56 11.24 12.67 28350 125.26 127.06 1.8 3.24 8.3 1159.0 128.6 126.50 2.06 13.24 18.79 15.73 35 550.45 9.21 322.43 248.72 28273 125.06 126.86 1.8 3.24 8.3 1159.0 128.4 126.30 2.06 13.24 18.79 15.34 77 1181.08 9.21 709.35 547.17 Sub Total 112 1731.53 1031.78 795.89 L_EMB5 (50 m)

19050 101.82 103.25 1.43 2.26 9.6 1681.6 104.8 102.90 1.85 12.40 16.09 19000 101.82 103 1.18 2.26 9.6 1681.6 104.5 102.90 1.60 11.40 13.12 14.61 50 730.37 7.16 357.76 303.88 Sub Total 50 730.37 357.76 303.88 L_EMB6 (82 m)

19000 101.82 103 1.18 2.26 9.6 1681.6 104.5 102.50 2.00 13.00 18.00 18918 101.32 102.5 1.18 2.26 9.6 1681.6 104.0 102.50 1.50 11.00 12.00 15.00 82 1230.00 6.71 550.06 480.03 Sub Total 82 1230.00 550.06 480.03 L_EMB7 (21 m)

18855 98.97 102.57 3.6 1.65 9.7 1664.8 104.1 102.00 2.07 13.28 18.92 18834 98.97 102.57 3.6 1.65 9.7 1664.8 104.1 102.00 2.07 13.28 18.92 18.92 21 397.32 9.26 194.40 149.70 Sub Total 21 397.32 194.40 149.70 L_EMB8 (36m)

18136 98.81 101.1 2.29 1.85 9.8 1631.5 102.6 101.00 1.60 11.40 13.12 18100 98.81 101.1 2.29 1.85 9.8 1631.5 102.6 100.75 1.85 12.40 16.09 14.61 36 525.87 8.27 297.84 238.92 Sub Total 36 525.87 297.84 238.92 L_EMB9 (265 m)

18040 98.42 101.01 2.59 1.37 9.8 1631.5 102.5 100.50 2.01 13.04 18.13 18000 98.42 101.01 2.59 1.37 9.8 1631.5 102.5 100.00 2.51 15.04 25.15 21.64 40 865.61 11.22 448.99 324.49 17900 98.5 100.94 2.44 1.14 9.8 1631.5 102.4 100.00 2.44 14.76 24.11 12.05 200 2410.72 10.91 2182.34 1591.17 7875 98.5 100.94 2.44 1.14 9.8 1631.5 102.4 100.50 1.94 12.76 17.23 8.61 25 215.34 8.68 216.89 170.95 Sub Total 265 3491.67 2848.22 2086.61 L_EMB10 (60m)

17300 97.5 99.95 2.45 2.97 9.9 1631.5 101.5 99.75 1.70 11.80 14.28 17240 97.5 99.95 2.45 2.97 9.9 1631.5 101.5 99.75 1.70 11.80 14.28 14.28 60 856.80 7.60 456.14 378.07 Sub Total 60 856.80 456.14 378.07 L_EMB11 (112m)

16765 96.6 98.89 2.29 1.87 10.0 1631.5 100.4 98.50 1.89 12.56 16.59 16653 96.6 98.89 2.29 1.87 10.0 1631.5 100.4 98.50 1.89 12.56 16.59 16.59 112 1858.55 8.45 946.63 753.32 Sub Total 112 1858.55 946.63 753.32 L_EMB12 (160 m)

16570 96.43 98.81 2.38 1.37 10.1 1631.5 100.3 98.25 2.06 13.24 18.79 16500 96.3 98.62 2.32 1.84 10.1 1631.5 100.1 96.00 4.12 21.48 54.55 36.67 70 2566.76 18.42 1289.72 819.86 16410 96.18 98.01 1.83 3.07 10.1 1631.5 99.5 97.50 2.01 13.04 18.13 9.07 90 815.86 8.99 808.98 629.49 Sub Total 160 3382.62 2098.71 1449.35 L_EMB13 (8m)

13890 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 93.25 2.01 13.04 18.13 13882 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 93.25 2.01 13.04 18.13 18.13 8 145.04 8.99 71.91 55.95 Sub Total 8 145.04 71.91 55.95 L_EMB14 (98 m)

13675 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 93.00 2.26 14.04 21.52 13625 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 93.00 2.26 14.04 21.52 21.52 50 1075.76 10.11 505.34 377.67 13577 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 93.00 2.26 14.04 21.52 19.82 48 951.49 10.11 485.12 362.56 Sub Total 98 2027.25 990.46 740.23 L_EMB15 (587 m)

13400 91 92.66 1.66 2.1 10.5 1535.1 94.2 92.00 2.16 13.64 20.13 13200 89.76 92.53 2.77 1.18 10.6 1535.1 94.0 91.50 2.53 15.12 25.45 22.79 200 4558.30 11.31 2262.83 1631.42 12813 89.21 92.06 2.85 1.61 10.7 1535.1 93.6 91.00 2.56 15.24 25.91 23.71 387 9176.23 11.45 4430.50 3182.75 Sub Total 587 13734.53 6693.33 4814.17 L_EMB16 (78m)

12126.6 88.48 91.17 2.69 1.98 10.8 1535.1 92.7 91.00 1.67 11.68 13.93 12048.6 88.48 91.17 2.69 1.98 10.8 1535.1 92.7 90.75 1.92 12.68 16.97 15.45 78 1205.12 8.59 669.73 529.86 Sub Total 78 1205.12 669.73 529.86 L_EMB17 (1879 m)

11950 88.48 91.17 2.69 1.98 10.8 1535.1 92.7 90.25 2.42 14.68 23.81 11800 88.43 90.9 2.47 1.63 10.9 1535.1 92.4 90.00 2.40 14.60 23.52 23.67 145 3431.63 10.73 1556.26 1140.63 11600 87.86 90.66 2.8 1.63 10.9 1535.1 92.2 90.00 2.16 13.64 20.13 21.83 204 4452.42 9.66 1970.54 1495.27 11343.4 87.81 90.31 2.5 1.61 10.9 1535.1 91.8 89.00 2.81 16.24 29.84 24.99 320 7995.74 12.57 4021.22 2810.61

11200 87.8 89.93 2.13 2.06 11.0 1535.1 91.4 88.30 3.13 17.52 35.24 32.54 160 5206.88 14.00 2239.58 1519.79 11000 87.57 89.49 1.92 1.8 11.0 1535.1 91.0 88.00 2.99 16.96 32.83 34.04 205 6977.59 13.37 2741.11 1883.06 10800 86.82 89.31 2.49 1.35 11.0 1535.1 90.8 87.00 3.81 20.24 48.08 40.46 215 8698.08 17.04 3663.24 2369.12 10600 86.72 88.91 2.19 2.11 11.0 1535.1 90.4 87.00 3.41 18.64 40.31 44.19 200 8838.84 15.25 3049.90 2024.95 10400 86.75 88.55 1.8 1.78 11.1 1535.1 90.1 87.20 2.85 16.40 30.49 35.40 140 4956.08 12.75 1784.33 1242.16 10200 86.1 88.46 2.36 1.07 11.1 1535.1 90.0 87.00 2.96 16.84 32.32 31.41 210 6595.91 13.24 2779.80 1914.90 10071 86.1 88.46 2.36 1.07 11.1 1535.1 90.0 87.50 2.46 14.84 24.40 28.36 80 2269.06 11.00 880.09 640.04 Sub Total 1879 59422.23 24686.07 17040.53 L_EMB18(1430 m)

10055 86.17 88.04 1.87 2.33 11.1 1535.1 89.5 87.25 2.29 14.16 21.94 9800 85.5 87.76 2.26 1.46 11.2 1535.1 89.3 85.80 3.46 18.84 41.24 31.59 247 7802.90 15.47 3821.86 2528.43 9584.82 84.83 87.46 2.63 1.79 11.2 1535.1 89.0 85.50 3.46 18.84 41.24 41.24 204 8413.61 15.47 3156.52 2088.26 9107.75 84.95 87.02 2.07 1.32 11.3 1535.1 88.5 85.00 3.52 19.08 42.38 41.81 408 17059.30 15.74 6422.51 4231.25 8687.83 84.41 86.77 2.36 1.17 11.4 1535.1 88.3 84.00 4.27 22.08 57.82 50.10 321 16081.55 19.10 6129.64 3867.32 8481.29 84.76 86.6 1.84 1.38 11.4 1535.1 88.1 83.50 4.60 23.40 65.32 61.57 192 11821.04 20.57 3949.67 2454.84 8481.29 84.76 86.6 1.84 1.38 11.4 1535.1 88.1 85.00 3.10 17.40 34.72 50.02 58 2901.16 13.86 804.07 547.03 Sub Total 1430 64079.56 24284.26 15717.13 L_EMB19 (320 m)

8323.2 83.71 85.79 2.08 3.09 11.4 1535.1 87.3 85.20 2.09 13.36 19.19 8263.2 83.71 85.79 2.08 3.09 11.4 1535.1 87.3 84.70 2.59 15.36 26.37 22.78 60 1366.57 11.58 694.95 497.47 8003.2 83.71 85.79 2.08 3.09 11.4 1535.1 87.3 85.00 2.29 14.16 21.94 28.33 260 7365.57 10.24 2662.63 1981.31 Sub Total 320 8732.14 3357.58 2478.79 L_EMB20 (491m)

7578.18 82.76 84.87 2.11 1.13 11.6 1646.1 86.4 84.00 2.37 14.48 23.08 7423.18 82.76 84.87 2.11 1.13 11.6 1646.1 86.4 83.80 2.57 15.28 26.06 24.57 151 3710.34 11.49 1735.45 1245.22 7083.18 82.76 84.87 2.11 1.13 11.6 1646.1 86.4 83.00 3.37 18.48 39.56 30.75 340 10455.34 15.07 5124.02 3412.01 Sub Total 491 14165.68 6859.47 4657.23 L_EMB21(1711 m)

6662.33 80.99 84.18 3.19 1.58 11.715 1646.104 85.7 83.20 2.48 14.92 24.70 6399.03 81.4 83.9 2.5 1.56 11.767 1646.104 85.4 83.00 2.40 14.60 23.52 24.11 265 6389.26 10.73 2844.19 2084.60 265 6125.61 81.31 83.73 2.42 1.22 11.818 1646.104 85.2 81.50 3.73 19.92 46.48 35.00 270 9449.43 16.68 4503.75 2926.88 535 5901.38 80.8 83.57 2.77 1.41 11.869 1646.104 85.1 81.00 4.07 21.28 53.48 49.98 220 10995.12 18.20 4004.23 2552.11 755 5584.02 81.1 82.54 1.44 3 11.921 1646.104 84.0 82.00 2.04 13.16 18.52 36.00 302 10872.45 9.12 2755.11 2132.55 1057 4774.8 80.41 82.28 1.87 0.6 12.109 1646.104 83.8 79.00 4.78 24.12 69.60 44.06 560 24673.60 21.38 11970.65 7385.32 1617 4674.8 80.41 82.28 1.87 0.6 12.109 1646.104 83.8 81.20 2.58 15.32 26.21 47.90 100 4790.48 11.54 1153.78 826.89 1717 Sub Total 1717 67170.34 27231.71 17908.35 L_EMB22 (173 m)

4456.7 80.2 82.2 2.0 1.2 12.2 1646.1 83.7 81.00 2.65 15.60 27.30 4283.7 79.1 81.8 2.7 1.6 12.3 1646.1 83.3 80.40 2.87 16.48 30.82 29.06 173 5027.28 12.83 2220.39 1542.70 Sub Total 173 5027.28 2220.39 1542.70 L_EMB23 (2517m)

4150.98 79.07 81.77 2.7 1.55 12.3 1646.1 83.3 80 3.27 18.08 37.74 3696.36 78.2 81.1 2.9 1.91 12.4 1646.1 82.6 80.2 2.40 14.60 23.52 30.63 372 11393.58 15.73 5852.60 2926.30 3082.72 78.38 79.75 1.37 1.68 12.6 1646.1 81.3 79.7 1.55 11.20 12.56 18.04 710 12806.62 11.93 8471.44 4235.72 2797.8 75.86 79.24 3.38 1.59 12.7 1646.1 80.7 78.5 2.24 13.96 21.24 16.90 350 5913.28 15.02 5256.05 2628.02 2211.62 76.48 79.03 2.55 0.73 12.8 1646.1 80.5 78.75 1.78 12.12 15.24 18.24 335 6109.06 12.96 4341.65 2170.83

2211.62 76.48 79.03 2.55 0.73 12.8 1646.1 81.0 79.5 1.50 11.00 12.00 13.62 750 10213.80 11.71 8781.00 4390.50 Sub Total 2517 46436.35 32702.74 16351.37 Grand Total 11511.0 329090.1 153694.4 99332.2

Table 5: Summary of Costs – Right Flood Embankment

Name of work: Construction of Embankment Location:- Jhapa district Name of River: Biring S.N Description of work Quantity Unit Rate Amount (NPR) Remarks Earthwork excavation in river bed material and filling with the same material compacted in 15 cm layers with optimum water content for 1 3 embankment construction including spreading & dressing of soil all 270399.67 m 94.76 25,622,266.58 complete as per design, drawing, and specification. Earthwork excavation in sweet soil (soft clay/silty soil) and filling with the same material compacted in15 cm layers with optimum water 2 3 content for embankment construction including transportation, 107634.01 m 180.45 19,422,625.78 spreading & dressing of soil all complete as per design,

Earthwork excavation in river bed material for launching 3 39600.00 53.64 2,123,985.60 foundation including disposal. (Machanical) m3

Supply of boulder & filling in Gabion box including placing in position, 4 42900.00 2944.66 126,326,072.21 tying gabion by tightning wire closing from top all complete . m3

Supply of 10 gauge medium coated G.I. wire and making 5 rectangular gabion boxes for mesh size 100 mm x 100 mm with two 239785.20 252.36 60,513,072.28 m2 way knot including wire cutting, netting etc. complete.

Supply & Laying of Geotextile Terram 2000 or Polyfelt Ts. 50 or 6 55000.00 120.14 6,607,759.12 equivalent., Mass per unit area > 150gm/m2 . m2 Grass sodding works including sod cutting, transporting, placing in 7 153694.44 40.28 6,191,503.80 position and water sprinkling with all lead and lift m2 8 Site clearance 1.00 job 150000.00 150,000.00 Temporarty diversion, dewatering work, temporary approach road 9 1.00 job 500000.00 500,000.00 repair/construction Sub Total of (C) Rs. 247457285.37

Table 6: Design of Right Flood Embankment

Top of Area= Quantity of Turfing Quantity of Quantity stn. In Min. ch. Water Qpeak RL of GL Ht. of Base Mean Area Length Depth (m) V (m/s) tpeak (hr) embank- (5h+2h*h) earth filling Length Turfing of sweet HEC-RAS El (m) level (m) (m3/s) (m) Emb(m) Width (m) (sq.m) (m) ment (m) (sq.m) (cu.m) (m) (sq m) (cu m) R_EMB1 (1180 m) 15965 95.48 97.3 1.82 3.07 10.1 1631.5 98.8 97.00 1.80 12.20 15.48 15430.37 93.44 96.62 3.18 1.54 10.2 1631.5 98.1 94.00 4.12 21.48 54.55 35.014 533 18662.68 18.42 9820.33 6242.67 15200 94.54 96.27 1.73 1.72 10.3 1631.5 97.8 94.75 3.02 17.08 33.34 43.945 235 10327.03 13.51 3173.78 2174.39 15000 93.54 95.78 2.24 2.07 10.3 1631.5 97.3 94.50 2.78 16.12 29.36 31.349 205 6426.50 12.43 2548.59 1786.80 14785 93.51 95.49 1.98 1.52 10.4 1631.5 97.0 95.50 1.49 10.96 11.89 20.624 207 4269.06 6.66 1379.30 1207.15 Sub Total 1180 39685.27 16922.00 11411.00 R_EMB2 (2355 m) 14540 92.51 95.06 2.55 1.96 10.4 1631.5 96.6 95.00 1.56 11.24 12.67 14400 92.4 94.74 2.34 1.94 10.4 1631.5 96.2 93.50 2.74 15.96 28.72 20.69 150 3103.68 12.25 1837.99 1294.00 14200 92.01 94.44 2.43 1.68 10.4 1582.6 95.9 93.00 2.94 16.76 31.99 30.35 210 6373.75 13.15 2761.01 1905.51 13800 91.9 93.76 1.86 1.92 10.5 1535.1 95.3 92.50 2.76 16.04 29.04 30.51 284 8665.18 12.34 3505.33 2462.67 13400 91 92.66 1.66 2.1 10.5 1535.1 94.2 91.90 2.26 14.04 21.52 25.28 313 7911.14 10.11 3163.40 2364.20 13200 89.76 92.53 2.77 1.18 10.6 1535.1 94.0 91.00 3.03 17.12 33.51 27.51 223 6135.51 13.55 3021.69 2068.34 12589.04 89.21 92.06 2.85 1.61 10.7 1535.1 93.6 90.20 3.36 18.44 39.38 36.45 695 25329.62 15.03 10443.01 6959.01 12309.0* 88.87 91.7 2.83 1.8 10.7 1535.1 93.2 90.00 3.20 17.80 36.48 37.93 310 11758.18 14.31 4436.22 2993.11 12130 88.68 91.47 2.79 1.98 10.8 1535.1 93.0 91.00 1.97 12.88 17.61 27.05 170 4597.80 8.81 1497.67 1173.84 Sub Total 2355 73874.86 30666.34 21220.67 R_EMB3 (698 m) 12021.64 88.48 91.17 2.69 1.98 10.8 1535.1 92.7 90.25 2.42 14.68 23.81 11800 88.43 90.9 2.47 1.63 10.9 1535.1 92.4 89.00 3.40 18.60 40.12 31.97 238 7608.00 15.20 3618.74 2404.37 11600 87.86 90.66 2.8 1.63 10.9 1535.1 92.2 89.00 3.16 17.64 35.77 37.95 211 8006.52 14.13 2981.75 2018.38 11343.41 87.81 90.31 2.5 1.61 10.9 1535.1 91.8 89.25 2.56 15.24 25.91 30.84 184 5674.41 11.45 2106.49 1513.25 11260 87.8 89.93 2.13 2.06 11.0 1535.1 91.4 89.50 1.93 12.72 17.10 21.50 65 1397.73 8.63 561.01 443.01 Sub Total 698 22686.67 9268.00 6379.00 R_EMB4 (3968 m) 11245 87.67 89.59 1.92 1.8 11.0 1535.1 91.1 89.00 2.09 13.36 19.19 11000 87.57 89.49 1.92 1.8 11.0 1535.1 91.0 88.50 2.49 14.96 24.85 22.018 45 990.82 11.14 501.09 363.04 10800 86.82 89.31 2.49 1.35 11.0 1535.1 90.8 87.20 3.61 19.44 44.11 34.482 200 6896.44 16.14 3228.78 2114.39 10600 86.72 88.91 2.19 2.11 11.0 1535.1 90.4 87.00 3.41 18.64 40.31 42.210 200 8442.04 15.25 3049.90 2024.95 10400 86.75 88.55 1.8 1.78 11.1 1535.1 90.1 86.90 3.15 17.60 35.59 37.951 245 9297.90 14.09 3451.27 2338.13 10200 86.1 88.46 2.36 1.07 11.1 1535.1 90.0 87.10 2.86 16.44 30.66 33.127 200 6625.42 12.79 2557.98 1778.99 10000 86.17 88.04 1.87 2.33 11.1 1535.1 89.5 87.00 2.54 15.16 25.60 28.131 210 5907.55 11.36 2385.36 1717.68 9800 85.5 87.76 2.26 1.46 11.2 1535.1 89.3 86.30 2.96 16.84 32.32 28.963 200 5792.64 13.24 2647.42 1823.71 9584.818 84.83 87.46 2.63 1.79 11.2 1535.1 89.0 86.00 2.96 16.84 32.32 32.323 240 7757.57 13.24 3176.91 2188.45 9107.752 84.95 87.02 2.07 1.32 11.3 1535.1 88.5 86.00 2.52 15.08 25.30 28.812 530 15270.36 11.27 5972.80 4311.40 8687.833 84.41 86.77 2.36 1.17 11.4 1535.1 88.3 85.10 3.17 17.68 35.95 30.624 405 12402.84 14.18 5741.38 3883.19 8481.29 84.76 86.6 1.84 1.38 11.4 1535.1 88.1 85.00 3.10 17.40 34.72 35.334 200 7066.78 13.86 2772.64 1886.32 8263.203 83.71 85.79 2.08 3.09 11.4 1535.1 87.3 85.00 2.29 14.16 21.94 28.329 200 5665.82 10.24 2048.18 1524.09 7423.177 82.76 84.87 2.11 1.13 11.6 1646.1 86.4 83.00 3.37 18.48 39.56 30.751 558 17159.06 15.07 8409.42 5599.71 6662.328 80.99 84.18 3.19 1.58 11.7 1646.1 85.7 82.50 3.18 17.72 36.12 37.844 460 17408.38 14.22 6541.64 4420.82 6587.328 81.2 84.08 2.88 1.56 11.8 1646.1 85.6 83.30 2.28 14.12 21.80 28.961 75 2172.06 10.20 764.71 569.86 Sub Total 3968 128855.67 53249.49 36544.75

Top of Area= Quantity of Turfing Quantity of Quantity Mean Area stn. In Min. ch. Water Qpeak embankm RL of GL Ht. of Base (5h+2h*h) Length earth filling Length Turfing of sweet Depth (m) V (m/s) tpeak (hr) (sq.m) Remarks HEC-RAS El (m) level (m) (m3/s) ent (m) (m) Emb(m) Width (m) (sq.m) (m) (cu.m) (m) (sq m) (cu m)

R_EMB5 (729 m)

6399.029 81.4 83.9 2.5 1.56 11.8 1646.1 85.4 83.00 2.40 14.60 23.52 6125.607 81.31 83.73 2.42 1.22 11.8 1646.1 85.2 81.20 4.03 21.12 52.63 38.076 284 10813.56 18.02 5118.29 3269.15 5901.378 80.8 83.57 2.77 1.41 11.9 1646.1 85.1 82.00 3.07 17.28 34.20 43.416 245 10636.87 13.73 3363.61 2294.31 5684.021 80.5 82.04 1.54 3 11.9 1646.1 83.5 82.00 1.54 11.16 12.44 23.322 200 4664.30 6.89 1377.38 1188.69 Sub Total 729 26114.73 9859.28 6752.14 R_EMB6 (595m)

4674.804 80.31 82.22 1.91 0.6 12.1 1646.1 83.7 80.00 3.72 19.88 46.28 4526.685 80.2 82.15 1.95 1.2 12.2 1646.1 83.7 80.00 3.65 19.60 44.90 45.586 170 7749.60 16.32 2774.88 1812.44 4150.984 79.07 81.77 2.7 1.55 12.3 1646.1 83.3 80.00 3.27 18.08 37.74 41.315 425 17559.05 14.62 6214.96 4169.98 Sub Total 595 25308.65 8989.84 5982.42 R_EMB7 (120m)

4000 78.67 81.47 2.8 1.55 12.3 1646.1 83.0 80.00 2.97 16.88 32.49 3880 78.57 81.37 2.8 1.55 12.3 1646.1 82.9 80.00 2.87 16.48 30.82 31.658 120 3798.94 12.83 1540.16 1070.08 Sub Total 120 3798.94 1540.16 1070.08 R_EMB8 (376m)

3736.357 78.1 81.2 3.1 1.91 12.4 1646.1 82.7 80.20 2.50 15.00 25.00 3696.357 78.2 81.1 2.9 1.91 12.4 1646.1 82.6 79.90 2.70 15.80 28.08 26.540 40 1061.60 12.07 482.98 341.49 3082.723 78.58 79.95 1.37 1.68 12.6 1646.1 81.5 79.00 2.45 14.80 24.26 26.167 336 8792.28 10.96 3681.35 2680.68 Sub Total 376 9853.88 4164.33 3022.16 R_EMB9 (67 m) 3082.723 78.58 79.95 1.37 1.68 12.6 1646.1 81.5 79.00 2.45 14.80 24.26 3015.723 78.42 79.9 1.48 1.68 12.6 1646.1 81.4 79.00 2.40 14.60 23.52 23.888 67 1600.46 10.73 719.10 527.05 Sub Total 67 1600.46 719.10 527.05 R_EMB10 (333m) 2817.803 75.86 79.24 3.38 1.59 12.7 1646.1 80.7 79.00 1.74 11.96 14.76 2558.803 76.58 79.13 2.55 0.73 12.8 1646.1 80.6 77.00 3.63 19.52 44.50 29.629 250 7407.37 16.23 4058.34 2654.17 2484.803 76.48 79.03 2.55 0.73 12.8 1646.1 80.5 78.30 2.23 13.92 21.10 32.800 83 2722.38 9.97 827.72 621.36 Sub Total 333 10129.76 4886.06 3275.53 R_EMB11 (528m) 2104.54 75.72 78.99 3.27 0.96 12.9 1646.1 80.5 78.50 1.99 12.96 17.87 1916.54 75.62 78.89 3.27 0.96 12.9 1646.1 80.4 77.00 3.39 18.56 39.93 28.902 182 5260.20 15.16 2759.13 1834.57 1576.54 75.52 78.79 3.27 0.96 12.9 1646.1 80.3 78.00 2.29 14.16 21.94 30.936 346 10703.93 10.24 3543.34 2636.67 Sub Total 528 15964.13 6302.48 4471.24 R_EMB12 (51m) 1258.341 76.18 77.82 1.64 3.17 13.1 1646.1 79.3 77.00 2.32 14.28 22.36 1207.341 76.18 77.82 1.64 3.17 13.1 1646.1 79.3 77.00 2.32 14.28 22.36 22.365 51 1140.60 10.38 529.13 392.06 Sub Total 51 1140.60 529.13 392.06 R_EMB13 (104m) 1158.341 76.18 77.82 1.64 3.17 13.1 1646.1 79.3 77.00 2.32 14.28 22.36 1054.341 76.18 77.82 1.64 3.17 13.1 1646.1 79.3 77.00 2.32 14.28 22.36 22.365 104 2325.94 10.38 1079.00 799.50

Sub Total 104 2325.94 1079.00 799.50 Top of Area= Mean Area Quantity of Turfing Quantity of Quantity stn. In Min. ch. Water Depth (m) V (m/s) tpeak (hr) Qpeak embankm RL of GL Ht. of Base (5h+2h*h) (sq.m) Length earth filling Length Turfing of sweet Remarks HEC-RAS El (m) level (m) (m3/s) ent (m) (m) Emb(m) Width (m) (sq.m) (m) (cu.m) (m) (sq m) (cu m)

R_EMB14 (600 m) 1054.341 75.68 77.52 1.84 3.17 13.1 1646.1 79.0 77.20 1.82 12.28 15.72 758.341 75.18 77.02 1.84 3.17 13.1 1646.1 78.5 75.00 3.52 19.08 42.38 29.053 300 8715.84 15.74 4722.43 3111.22 668.9421 74.86 76.57 1.71 1.16 13.2 1646.1 78.1 75.20 2.87 16.48 30.82 26.594 300 7978.29 12.83 3850.39 2675.20 Sub Total 600 16694.13 8572.82 5786.41 Grand Total 11704 378033.68 156748.03 107634.01

Table 7: Summary of Costs – Solid Spurs

Name of work: Construction of Solid Spur

Location:- Jhapa district

Name of River:Biring

Item Nos. Description of work Quantity Unit Rate Amount (NPR) Remarks

Civil Work Earthwork in excavation in soft clay & silty soil / 1 gravel mixed soil including disposal (10m lead and 120.49 3 290.20 34,965.77 m 1.5m lift) Supply of boulder and filling in gabion boxes 2 including placing in position, laying, tying and closing 267.98 3 2944.66 789,095.26 m from top etc. all complete. Supply & Weaving of 10 SWG G.I. Wire for making rectangular gabion boxes of different sizes having 3 1386.45 2 252.36 349,884.52 mesh size (10 x 10)cm. with double knotting m including wire cutting netting

4 Site clearance 1.00 job 50000.00 50,000.00

1,223,945.55

Sub Total ) Rs. 1223945.55 Left bank (26 nos) 26 31822584.30 Right bank (24 nos) 24 29374693.20 Total 61197277.50

ANNEX 6: BIRING PRIORITY BASIN PREFEASIBILITY COST-BENEFIT ANALYSIS REPORT

ABBREVIATIONS

ADB Asian Development Bank AEP Annual Exceedance Probability APL Annual Probability of Loss BoQ Bill of Quantity BCR Benefit-Cost Ratio CBA Cost-Benefit Analysis CBS Central Bureau of Statistics CC Climate Change DAP Di-ammonium phosphate DEM Digital Elevation Model DoI Department of Irrigation DWIDP Department of Water Induced Disaster Prevention EIRR Economic Internal Rate of Return EIRR Financial Internal Rate of Return EM-DAT Emergency Events Database ENPV Economic Net Present Value EWS Early Warning System FHFRM Flood Hazard/Flood Risk Map FHR Flood Hazard Rating FNPV Financial Net Present Value GIS Geographical Information System IRR Internal Rate of Return kcals Kilo-calories MoHA Ministry of Home Affairs NPR Nepalese Rupees NPV Net Present Value RCC Reinforced Concrete SER Shadow Exchange Rate SERF Shadow Exchange Rate Factor SWRF Shadow Wage Rate Factor

Assumed Rate of Exchange: NPR 106 ~ US$ 1

ANNEX 6

BIRING PRIORITY BASIN: PREFEASIBILITY COST-BENEFIT ANALYSIS REPORT

CONTENTS

I. METHODOLOGY OF THE CBA ...... 1

II. HOUSING AND INFRASTRUCTURE ...... 3 General Description of Biring Priority Basin ...... 3 Estimate of Housing and Infrastructure Affected ...... 3 Estimate of the Quality of Housing ...... 8 Estimate of the Loss of Value of Housing to Floods ...... 11 Estimate of the Cost of Damage to Building Occupants ...... 13 Estimate of the Damage to Public Infrastructure ...... 14 Estimate of Infrastructure Direct Losses Without and With-project ...... 14 Indirect Benefit from Increased Infrastructure Development With-project ...... 18 Indirect Benefit from Increased Infrastructure Development With and Without- project ...... 18

III. AGRICULTURE ...... 20

IV. MORTALITY AND MORBIDITY ...... 29

V. LIVESTOCK ...... 32

VI. FLOOD PREVENTION AND COPING COSTS ...... 35

VII. CBA WITH IMPACT OF CLIMATE CHANGE ...... 40 CBA in Financial Prices ...... 40 CBA in Economic Prices ...... 43

VIII. RESULTS AND SENSITIVITY ANALYSIS OF THE CBA ...... 49 Summary of the Financial and Economic Indicators ...... 49 Sensitivity Analysis ...... 50 Poverty ...... 52

LIST OF FIGURES

Figure 1: Housing Affected, Damaged/Destroyed by Flood Return Period ...... 7 Figure 2: Proposed Flood Mitigation Measures ...... 15 Figure 3: Flood Damage Curve Without and With-project - Housing and Infrastructure ...... 17 Figure 4: Flood Damage Curve Without and With-project: Agriculture ...... 27 Figure 5: Mortality and Morbidity Rates by Magnitude of Flood Damage ...... 30 Figure 6: Flood Damage Curve Without and With-project: Casualties ...... 31 Figure 7: Livestock Mortality Rates by Magnitude of Flood Damage ...... 33 Figure 8: Flood Damage Curve Without and With-project: Direct Loss of Livestock Value ...... 34 Figure 9: Flood Damage Curve Prevention and Coping Costs ...... 38

LIST OF TABLES

Table 1: Population and Land Use ...... 3 Table 2: Change in House Numbers, 1992-2012 ...... 4 Table 3: Estimated Distribution of Houses by Flood Envelope, 2012 ...... 5 Table 4: Historical House Damage ...... 5 Table 5: Linear Regression, Houses Affected on Flood Return Period and Housing Density ... 6 Table 6: Regression of Proportion of Poor Quality Housing on Proportion of VDC Area in Flood Risk Area ...... 9 Table 7: Revision of Estimated Number of Poor Quality Houses in Flood Affected Areas ...... 10 Table 8: Housing by House Quality Class: Priority Basins ...... 10 Table 9: Reduction of House Depreciated Value in Response to FHR ...... 12 Table 10: Numbers of Displaced, Assisted and Re-settled Households ...... 13 Table 11: Summary of Without-project Direct Costs by Flood Return Period: Infrastructure ..... 14 Table 12: Number and Distribution of Unprotected Houses With-project ...... 16 Table 13: Summary of With-project Direct Costs by Flood Return Period: Infrastructure ...... 17 Table 14: Processed Without and With-project Direct and Indirect Benefits from Infrastructure19 Table 15: Without-project, Without-flood Gross Margins for Paddy Rice Technologies ...... 21 Table 16: With-project, Without-flood Gross margins for Paddy Rice Technologies ...... 22 Table 17: Present Without-project and Expected Future With-project Cropping Pattern ...... 23 Table 18: Expected Loss of Yield of Rice Depending on Flood Date ...... 23 Table 19: Without-project Gross Margin and Production on Flood Affected Land Without and With-Project by Flood Return Period ...... 25 Table 20: Summary of Without-project Direct Costs by Flood Return Period: Agriculture ...... 26 Table 21: Summary of With-project Direct Costs and Indirect Benefits by Flood Return Period: Agriculture ...... 26 Table 22: Processed Without and With-project Direct and Indirect Benefits from Agriculture ... 28 Table 23: Estimate of Mortality and Morbidity by Magnitude of Flood Damage ...... 29 Table 24: Numbers of Dead, Missing and Injured by Modelled Basin, 1991-2015 ...... 30 Table 25: Annual Probability of Casualties Saved ...... 31 Table 26: Processed Without and With-project Indirect Loss from Livestock...... 34 Table 27: Summarised Food Budget Per Capita ...... 36 Table 28: Estimate of Crop Production Required to meet Annual Dietary requirement ...... 37 Table 29: Without-project Direct and Indirect Costs of Flood Prevention and Coping ...... 37 Table 30: With-project Direct and Indirect Costs of Flood Prevention and Coping ...... 38 Table 31: Processed Without and With-project Direct and Indirect Costs: Prevention and Coping ...... 39 Table 32: Cost:Benefit Analysis –Floods with Climate Change: Constant 2015 Financial Prices, NPR million ...... 42 Table 33: Estimate of SER, SERF and SCF for Nepal, 2010-2015 ...... 44 Table 34: Import Parity Price for Rice ...... 46 Table 35: Economic Conversion Factors for Costs and Benefits of Flood Management Project46 Table 36: Cost-Benefit Analysis – Floods with Climate Change: Constant 2015 Economic NPR million ...... 48 Table 37: Financial and Economic Indicators: Biring Sub-project ...... 49 Table 38: Impact of Change in FHR on Economic IRR ...... 50 Table 39: Impact of Change in Houses and Agricultural Area Affected on Economic IRR ...... 50 Table 40: Impact of Change of Houses Affected on Economic IRR ...... 51 Table 41: Biring Priority Basin Historical Flood Damage Data 1992-2015 ...... 53

I. METHODOLOGY OF THE CBA

1. A general model was prepared for the calculation of incremental avoided losses and incurred benefits between the without-project situation and the with-project (with flood management) situation for all six Priority Basins. Based on this model (which is described in the Mid-term Report but has since been modified), this report describes the result of a prefeasibility cost-benefit analysis for Biring Priority Basin.

2. The model estimates direct losses (losses incurred directly as a result of the flood event) and indirect losses and benefits (losses and benefits incurred as a result of changes in market conditions, technology and investment) under the flood regimes expected in the without-project present and future with-project situations. Indirect losses and benefits are estimated under the headings of infrastructure, agriculture, human mortality, livestock mortality and a miscellaneous heading entitled “prevention and coping mechanisms”. The discussion of project benefits in this report is broadly organized under these headings.

3. The model incorporates the expected costs of the proposed flood management project.

4. The incremental benefit of the flood management project is the difference between avoided losses in the without and with-project situations, plus indirect benefits obtained as a result of the proposed project. Avoided losses are weighted by the probability of their future occurrence 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 benefits, an incremental 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).

5. The expected benefit from saving of human life and injury as a result of the flood management project is also calculated. 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.

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

7. The Flood Hazard Rating (FHR) is a 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 area. 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. For floods of defined probabilities, the FHR is weighted to a single value for the envelope. 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. 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

8. The weighted FHR values are then used in Lookup Tables to give an estimate of damage to house (by four different types of house and public infrastructure) and yield reduction of paddy rice. The Lookup Tables are shown in Table 9 and Table 18.

9. 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 usually larger and the FHR is higher. This has implications for both project costs and benefits.

10. 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.

11. Cost-benefit analysis is required at pre-feasibility level for six Priority Basins to contribute to six separate Concept Papers for the development of possible flood management projects. The requirement for stand-alone documentation for each Priority Basin leads to repetition in the reports. However, in order to be useful to develop each potential project to feasibility level this is inevitable.

II. HOUSING AND INFRASTRUCTURE

General Description of Biring Priority Basin

12. Biring is a medium sized Priority Basin with a predicted historical 1 in 100 year flood envelope of 3,600 ha. Population density is low, at 2.2 persons per ha and the population is rural; there are no municipalities within the Basin. Housing is concentrated in the 1 in 2 year and I in 5 year flood envelopes. The arable area is 75% of the total 1 in 100 year envelope so the proportion of river channel and uncultivable bare areas is relatively large. All of this is classified as agricultural and is presumed to be in farms. The amount of agricultural land per rural house is reasonably large at 1.4 ha, but there is no opportunity to expand the cultivated area and agricultural productivity growth would have to be through intensification. Table 1 shows estimated population and land use statistics by flood envelope.

Table 1: Population and Land Use

Flood envelope of historical floods

1 in 1 in 1 in 1 in 1 in 1 in 2yr 5yr 10yr 25yr 50yr 100yr

Population 3,000 7,000 7,000 8,000 8,000 8,000

Urban ------

Rural 3,000 7,000 7,000 8,000 8,000 8,000

Population density, 2.30 2.49 2.34 2.47 2.35 2.24 persons per ha

Houses in incremental envelope 786 706 78 168 102 83 ha

Area, ha 1,307 2,809 2,992 3,239 3,408 3,578

Arable area, ha 739 2,067 2,214 2,411 2,549 2,692

Agricultural area, ha 730 2,054 2,202 2,398 2,536 2,730

Agricultural land as % of arable 99% 99% 99% 99% 100% 101%

Agricultural ha per rural house 0.93 1.38 1.40 1.38 1.38 1.42

Estimate of Housing and Infrastructure Affected

13. A flood management project should seek to reduce direct loss of infrastructure (mostly private housing and supporting public infrastructure) from flood events. Annual damage to infrastructure from historical floods is well documented in MOHA/DWIDP reports (see Table 41), but on its own the historical record is an inadequate guide to the infrastructure that would be affected by a flood event of exactly the same hydrological characteristics today, because of the increase in investment, particularly housing numbers, within the affected area over time. A method to disaggregate flood risk and flood vulnerability over time was developed to make forecasts for future infrastructure losses on the basis of the present infrastructure stock.

14. The Consultants obtained the number of houses by VDC and ward for 1991 from GIS imagery sources1 and compared it with the number of houses reported by VDC and ward in the Population and Housing Census 2011. Housing in VDC associated with the Biring modeled basin increased by about 67% during the period. See Table 2.

Table 2: Change in House Numbers, 1992-2012

Change in Number of Houses 1992-2012

Total by VDC Number in 1:100 Total by VDC in Number in 1:100 in model year envelope, model basin, year envelope, VDC basin, 1992 1992 2012 2012

Arjundhara 1,003 114 2,085 203 Chakchaki 632 12 763 8 Dangibari 670 92 751 117 Ghailadubba 1,394 307 2,509 606 Gherabari 82 14 140 24 Rajgadh 1,465 67 2,491 92 Sharanamati 1,170 436 1,887 755 Surunga 254 62 486 119 Total 6,670 1,104 11,112 1,923

15. Then, it was assumed that the same proportion of 2012 house numbers would be located in flood-prone areas as observed in 1992. This is expected to be a conservative estimate, because with increasing housing density in the project area as a whole, the proportion of housing in areas at risk from flooding should increase rather than decline. The figures suggest only a 174% increase in housing in the historical 1 in 100 year flood envelope in the last 20 years: an average annual growth rate of 3.1%. The data suggest that the number of houses on the floodplain is not only much smaller than (for example) Mawa Ratuwa Priority Basin, but it is also growing more slowly. Even so, continuing the trend of growth, an average rate of growth of housing over the life of the project might conceivably be 1.7% per annum or 148% over a 25-year period. At the end of this period the average area per house would still be about 1.25 ha. This is important when considering the future development that a flood management project would protect, even excluding growth stimulated by the protection itself: see section I for further development of this.

16. More can be done with the data. The area of the 1 in 100 year flood envelope is known by VDC and ward and the number of houses is estimated (also by ward) so the housing density at ward level in 2012 can be calculated. Then, taking the incremental area between the flood return period classes and applying the housing density applicable to each ward, the number of houses in each flood envelope can be calculated. See Table 3. It is apparent that 78% of

1 Source: Esri, Digital Globe Earthstar Geographics

2012 house numbers are estimated to be located in the 1 in 2 year and 1 in 5 year flood envelopes – the vast majority of houses are exposed to regular flood events.

Table 3: Estimated Distribution of Houses by Flood Envelope, 2012

Total 1 in 100yr 1 in 50yr 1 in 25yr 1 in 10yr 1 in 5yr 1 in VDC houses, to 50yr to 25yr to 10yr to 5yr to 2yr 2yr 2012 Arjundhara 13 14 23 13 43 96 203 Chakchaki 0 0 0 0 2 5 8 Dangibari 5 5 8 3 72 23 117 Ghailadubba 24 31 53 23 179 296 606 Gherabari 1 1 2 1 11 9 24 Rajgadh 3 3 6 3 43 35 92 Sharanamati 22 30 52 29 300 321 755 Surunga 16 17 23 6 54 2 119 Total 83 102 168 78 706 786 1,923 % 4% 5% 9% 4% 37% 41% 100% Area in ha 122 158 277 128 1,531 1,381 3,596

17. A number of possible explanations can be suggested for this distribution. Firstly, the two envelopes account for 81% of the area inside the 1 in 100yr envelope, so most of the estimated 1,920 houses in the flood plain will be located within it. Secondly, changes in flood risk may have subsequently exposed houses to a greater risk than perceived when they were first sited. Thirdly, the 2012 housing estimate is based on the known 1992 distribution, but some houses may have been destroyed by floods in the last 20 years.

18. The importance of this information is that it enables the number of houses affected by historical flood events, reported between 1991-2015, to be matched more precisely with the year in which each reported flood occurred. See Table 41 for the Biring flood reports, which were compiled from MOHA/DWIDP data. This information may be of further use at feasibility study stage, particularly after obtaining confirmation and supporting details from local sources,

19. The team’s Consultant Hydrologists calculated the annual maximum flow in the Biring River and the likely return period of the resulting flood, which was presumed to be the maximum flood for the year. For Biring, hydrological data is available for the period 1980-2009. Historical floods after this year could not be assigned a return period. However, for annual maximum historical floods 1991-2009 an indicative return period could be assigned, and then matched to the infrastructure losses reported in the historical record. See Table 4.

Table 4: Historical House Damage

Year Houses damaged Return period House population Housing Density, ha per house 1999 7 2.6 157 0.8 2000 11 1.5 102 0.8 2002 8 1.2 454 4.1 2003 34 2.5 844 2.0 2008 27 1.25 606 2.4 2009 813 66.7 1922.5 0.5

20. Housing density was difficult to derive, and required matching the ward area reported as affected by the flood (not easy, because the historical flood record is incomplete in this respect, particularly for the larger floods, see Table 41 with the interpolated number of houses by ward in the year of the flood. Each flood affected several or even many wards, and so housing density in each ward provided a weight for the total reported affected area.

Table 5: Linear Regression, Houses Affected on Flood Return Period and Housing Density

21. Then a linear regression was carried out, with number of houses damaged or destroyed as the independent variable and flood return period and housing population as the explanatory variables. The result is reported in Table 5. The level of explanation is high (R2=0.99). The coefficients of the flood return period is significant at 5% probability. The coefficient of housing population is statistically weak, but the sign is positive as expected. The very small number of observations is unfortunate (at least for the analysis, if not the population). The relationship is useful, because it provides a basis for estimating the number of houses affected by a flood of any given magnitude up to 1:66yr - providing the house population is known.

22. The number of houses damaged or destroyed from the 2012 housing stock was then estimated using the derived coefficients and compared with the house population and affected housing, as shown in Figure 1:. The house “population” by flood envelope is reported in Table 3: the Table shows incremental house numbers by envelope, clearly the population would be cumulative. Affected houses are located in those wards reported as affected by historical flood events as shown in Table 4. Only a proportion of affected houses are actually damaged or destroyed, as specified by the regression equation. The equation subsumes variations in house quality and variation in the flood hazard index during a flood event. Clearly some houses in a flood-affected area will resist a flood while others are damaged or destroyed.

Figure 1: Housing Affected, Damaged/Destroyed by Flood Return Period

23. With frequent flood events, not all the population of houses is affected. A possible explanation is that houses are preferentially located in the 1:2yr and 1:5yr envelopes (rather than envelopes of more infrequent but more severe floods) because floods over the whole envelope are not in fact an alternate year event. That they are not as strongly suggested by the historical flood damage record. Small floods, resulting from local rainfall in sub-basins close-by, are a manageable hazard for house owners and farmers. Flood damage records 1992-2015 do not report a 1:2yr flood (as classified by return periods from historical events) in any Priority Basin much larger than a few hundred hectares affecting only one or two wards.

24. However, as flood return period increases, a greater proportion of the house population of the envelope is affected. The interpolation up to the 1:100yr event is unsupported by observation but the model suggests that as a result of an historical 1 in 100 year event, all houses would be affected out of the housing stock of about 1,920 units. Of these, about 62% houses would be destroyed.

25. The calculation of houses damaged or destroyed was prepared outside the model for the calculation of avoided losses and incurred benefits, but Figure 1: provides the essential input for each flood envelope of the number of houses affected by the specified flood event and the proportion of those that are damaged or destroyed.

26. It may also be observed that the historical growth of housing is an indicator of the rate of change of investment in the basin modeled area in the future. As discussed in paragraph 56, the CBA takes account of this by assuming this investment will double over the life of the project. Over the last 20 years the housing stock appears to have expanded in ward areas at about 4.7% per annum (see Table 2), but the future rate of expansion is predicted to be only

about 1.7% (see above), so this assumption is probably over-optimistic. Even so, it is not enough to secure a satisfactory economic rate of return for the Biring project, see section VIII.

Estimate of the Quality of Housing

27. The Population and Housing Census 2012 compiles a count of housing at VDC level by type of foundation and type of wall material. The Census does not provide cross-tabulated data on number of houses by foundation and wall type but this is unimportant because the interest for this study is to derive a rating of house quality for the VDC as a whole. This was done as follows.

Foundation quality is rated as follows: Class 1: Cement bonded bricks and stone Class 2: Mud bonded bricks and stone and RCC pillars Class 3: Wooden pillars Class 4: Other foundations and not stated

Wall quality is rated as follows: Class 1: Cement bonded bricks and stone Class 2: Mud boned brick and stone Class 3: Wood and/or planks Class 4: Bamboo, unbaked brick, others and not stated

Class 1, 2, 3 and 4 are then summed and weighted by the total housing stock to get an aggregate housing quality classification for each VDC in Priority Basins.

28. The total area of each VDC is known from the Census. The proportion of each VDC in the flood-affected area (using the historical 1 in 100yr return period) of each Priority Basin is known from the Study GIS. Reason suggests that house quality should be poorer on flood plains than in non-flood affected areas: with the growth of population, households with fewer resources are marginalized in higher risk areas, the investment in housing at risk from flooding will be lower and past flood damage will lower the value of the housing stock. The data available enables a test of this assumption: the higher the proportion of a VDC in the flood affected area then the higher the proportion of poor quality housing in the VDC.

29. To test the assumption, the proportion of poor quality housing (Class 3 and 4 together) was regressed on the proportion of VDC area within the area affected by floods in 1 in 100yr. The results below show a positive correlation at 90% probability (and in fact close to 95% probability) that the proportion of “poor quality” housing (Classes 3 and 4 combined) is greater in VDCs with a higher proportion of village area on land prone to flooding.

Table 6: Regression of Proportion of Poor Quality Housing on Proportion of VDC Area in Flood Risk Area

30. For every percentage increase in VDC area at risk from flooding the proportion of poor quality houses increases by 0.29%. Also, the highly significant intercept shows that in the VDCs associated with the Priority Basins, at least 52% of housing will be of poor quality, even if little or none of the VDC falls in the flood-affected area. It is straightforward to use this relationship to revise the proportion of poor quality housing by VDC area (depending on the proportion of the VDC within the flood-affected area) and introduce this proportion in the calculation of the value of houses damaged or destroyed by floods.

31. It may be argued that the introduction of an estimate of the proportion of poor quality housing into the VDC area, instead of using the known and perfectly robust statistic for that VDC from the Population and Housing Census, introduces an estimate where no estimate is required. Using the Census figures directly would, overall, classify 52% of housing within the flood-affected areas as poor quality (that is, the same as reported by the Population and Housing Census for those VDC). But it has already been shown that statistically this would be an under-estimate of poor quality housing in flood-affected areas. Furthermore, the error would inflate the estimated value of losses from floods because a larger proportion of good quality housing would be assumed damaged/destroyed in a flood event. And finally, and more importantly, a poverty analysis would under-estimate the proportion of poor quality housing affected by floods, and therefore under-estimate the proportion of relatively poor households benefiting from flood management.

32. Having obtained the coefficients from the whole data set of all the VDC associated with all Priority Basin areas (Table 6), they were then applied to the percentage of each VDC within the flood affected areas in order to obtain an estimate of the proportion of poor quality housing, see the tabulation below.

Table 7: Revision of Estimated Number of Poor Quality Houses in Flood Affected Areas

Total Number of Class 3 & 4 number of houses on houses from Revised houses in flood plain, Housing Class 3 & 4 % VDC 2012 2012 Census houses change change East Rapti 78,519 3,976 1,873 2,297 424 23% West Rapti 46,092 5,103 3,058 3,275 217 7% Mawa Ratuwa 42,622 5,436 3,533 3,342 -190 -5% Biring 25,956 1,923 1,305 1,218 -86 -7% Lakhandehi 35,095 3,567 2,550 2,398 -152 -6% Mohana 41,465 2,282 1,257 1,293 36 3% Total 269,749 22,287 13,575 13,823 248 2%

33. About 8% of households in VDC (that contain some part of the Priority Basins) are on the flood plain. Using the VDC data from the Population and Housing Census, 61% of these would be classified as poor quality (Class 3 or 4). Adjusting the estimate of poor quality housing for the proportion of VDC within the flood plain increases this overall to 62%.

34. This may seem a meager adjustment, but there are differences between Priority basins. Both East and West Rapti have significant upward adjustments to the numbers of poor quality houses. Not all Priority Basins show the expected relationship after applying the adjustment. The Population and Housing Census shows that Mawa Ratuwa, Biring and Lakhandehi have a very large proportion of poor quality housing (bamboo walls on wooden pillar foundation) in the VDCs (65-71%); adjusting this proportion by coefficients derived from the whole data set actually reduces the high proportion observed by the Population and Housing Census. But the reduction is small compared with the strong upward adjustment required for East and West Rapti. So for Mawa Ratuwa, Biring and Lakhandehi the unadjusted housing quality classes can be used when estimating housing damage from floods.

35. It is straightforward to re-compile the adjusted data into the four classes of housing already classified (taking into account both foundation and wall quality), see Table 8. These percentages of house quality were used in the general model to represent the value of the current housing stock. The building classes “public buildings” and “other buildings” originally considered important in the flood damage estimate were eliminated. There is no data on their distribution in Priority Basins and their location in the flood plain is unlikely, though there are a few reports of schools and clinics affected by floods in the MOHA flood damage records. Such buildings can be located as part of feasibility study preparation.

Table 8: Housing by House Quality Class: Priority Basins

East Rapti West Rapti Mawa Ratuwa Biring Lakhandehi Mohana Class 1 22% 6% 27% 26% 25% 31% Class 2 22% 30% 11% 10% 8% 11% Class 3 33% 35% 32% 26% 33% 55% Class 4 24% 30% 29% 38% 35% 3%

36. A feasibility study would field-sample and classify the housing stock in more detail. The CBS classification may be sub-divided to capture differences in quality of housing construction rather than be based simply on building materials.

Estimate of the Loss of Value of Housing to Floods

37. Houses in the four classes at were assigned a depreciated value as follows:

Class 1: NPR 454,250 Class 2: NPR 337,500 Class 3: NPR 300,563 Class 4: NPR 200,357

The derivation of these rates is described in the Socio-economic Report for each Priority Basin. At feasibility stage these rates should be checked carefully by field survey on a sample of houses in the flood affected area. The result of the cost-benefit analysis is expected to be sensitive to the values of housing damaged and destroyed.

38. It is also necessary to identify the level of damage a house will sustain under floods of pre-defined characteristics. This is difficult and, without empirical data, contentious. Nevertheless, note from section A that the number of houses damaged or destroyed under floods of defined return period and known housing stock has already been specified by regression coefficients derived from observed data. The remaining issue is only to identify the level of damage and the proportion of damaged/destroyed houses actually destroyed. The Project GIS was used to establish the number of houses within each FHR area. Obviously this could only be done for the 1992 housing stock – the location of housing in 2012 is unknown. The FHR is useful to do this, see Table 9. Note the loss of value of public infrastructure due to flood damage is discussed below in paragraphs 46 and 47.

39. Obviously no damage is assumed with a rating of 0: there is no velocity, no depth and no debris. It is assumed a Class 4 house will be destroyed when the rating equals 1.5, which signifies a flood of 0.4 m depth. 0.75 m/sec velocity and debris factor of 1. This is classified as a “significant” flood hazard.

Table 9: Reduction of House Depreciated Value in Response to FHR

FHR Class 4 Class 3 Class 2 Class 1 Public Infrastructure 0 0% 0% 0% 0% 0% 0.5 0% 0% 0% 0% 6% 0.6 10% 5% 4% 4% 13% 0.7 20% 10% 8% 8% 19% 0.8 30% 15% 12% 12% 25% 0.9 40% 20% 16% 16% 31% 1 50% 25% 20% 20% 38% 1.1 60% 30% 24% 24% 44% 1.2 70% 35% 28% 28% 50% 1.3 80% 40% 32% 32% 56% 1.4 90% 45% 36% 36% 63% 1.5 100% 50% 40% 40% 69% 1.6 100% 55% 44% 44% 75% 1.7 100% 60% 48% 48% 81% 1.8 100% 65% 52% 52% 88% 1.9 100% 70% 56% 56% 94% 2 100% 75% 60% 60% 100% 2.1 100% 80% 64% 64% 100% 2.2 100% 85% 68% 68% 100% 2.3 100% 90% 72% 72% 100% 2.4 100% 95% 76% 76% 100% 2.5 100% 100% 80% 80% 100% 2.6 100% 100% 84% 84% 100% 2.7 100% 100% 88% 88% 100% 2.8 100% 100% 92% 92% 100% 2.9 100% 100% 96% 96% 100% 3 100% 100% 100% 100% 100% 3.1 100% 100% 100% 100% 100% 3.2 100% 100% 100% 100% 100% 3.3 100% 100% 100% 100% 100% 3.4 100% 100% 100% 100% 100% 3.5 100% 100% 100% 100% 100%

40. Class 3 housing is more resilient and will survive a FHR up to 2.5, classified as “extreme”. Classes 1 and 2 are assumed to resist up to a FHR of 3. Without empirical data, the amount of damage sustained to the house at lesser FHRs is only conjecture, so the percentage of damage sustained to the depreciated value of the house is assumed to be in direct proportion to the FHR.

Estimate of the Cost of Damage to Building Occupants

41. Impact on occupants must be added to the loss of value of buildings from flood damage. Four categories of direct loss are envisaged. Firstly, those households suffering a loss of value of less than 25% of the value of the house in which they live are assumed to stay in place during the flood event. They will therefore not incur a displacement cost.

42. The “displaced population” are those living in housing which is more than 25% damaged during a flood event. It is assumed that this population will leave the property and incur a disturbance allowance per household that is estimated to be 5% of the value of the house in which they live. See paragraph 37 for the value of the house by class.

43. Households suffering a loss in value of the house of >50% will be entitled to emergency assistance, a cost incurred by the local administration which is estimated to be NPR 2,080 per person. The household will repair the house at its own expense in the future so will not be entitled to re-settlement.

44. Households for which the house is 100% damaged by a flood event will re-settle elsewhere, or re-build their assets in the same place, either with or without Government support. If they settle elsewhere, the re-housing cost is estimated as NPR 200,000. Land acquisition is estimated to be NPR 250,000 per ha and an allowance of 0.2 ha has been budgeted. These estimates have been obtained from Government norms and the source is described in the Socio-economic Report.

45. The numbers of households affected in the ways described above as shown in Table 10.

Table 10: Numbers of Displaced, Assisted and Re-settled Households

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% <25% of house value lost: not HH 0 0 0 0 0 0 displaced >25% of house value lost: HH 8 48 110 296 605 1,195 displaced >50% of house value lost: HH 5 30 69 184 377 745 displaced and entitled to relief 100% of house value lost: displaced, entitled to relief and HH 3 18 41 111 228 450 resettlement Total damaged/destroyed HH 8 48 110 296 605 1,195 housing Undamaged housing HH 92 205 276 488 841 728 Total affected housing HH 100 253 386 783 1,446 1,923 Housing total population HH 786 1,492 1,570 1,738 1,840 1,923

Estimate of the Damage to Public Infrastructure

46. There will be damage to public infrastructure within the flood envelope, particularly for large-magnitude floods. Physical quantities of made and local roads, bridges, power lines and telephone lines were estimated on a per unit per house basis: i.e. the quantity of public infrastructure is based on the numbers of houses within the flood envelope. In Biring Priority Basin it is assumed there are:

0.5 metres of made road per housing unit 15 meters of un-made road per housing unit 0.005 bridges adequate for motor vehicles 2.5 meters of power line per housing unit and 1 meter of telephone line per housing unit.

47. The value of public infrastructure as a percentage of total infrastructure (including housing) value is estimated to be 9%. The value of public infrastructure damaged by a flood is the equivalent to the cost of repairing or replacing it after a flood event. Construction cost is estimated to be:

Made roads: NPR 5.20 million per km Farm roads: NPR 1.04 million per km Bridges: NPR 0.50 million each Power 33 kva NPR 3.12 million per km Telephone lines: NPR 1.04 million per km

The assumed repair or replacement cost as a percentage of the construction cost is shown in Table 9.

Estimate of Infrastructure Direct Losses Without and With-project

48. A summary of direct losses without-project is presented by flood return period in Table 11. Note that all valuations in the following tables specifying losses by flood event are in financial, not economic prices. Damage to housing is in all events the greatest loss, but displacement costs are also significant. In the event of severe damage to the housing stock the costs of relief and re-settlement are also high. These costs may not actually be incurred if Government resources are inadequate, particularly the cost of provision of new housing and land for the re-settled population. Nevertheless, it is important to identify relief and resettlement as a significant potential cost.

Table 11: Summary of Without-project Direct Costs by Flood Return Period: Infrastructure

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Damage to housing NPR m 2.73 17.11 37.61 102.71 208.41 400.22 Displacement costs NPR m 0.14 0.79 1.74 4.76 9.65 18.53 Relief and resettlement NPR m 0.97 5.33 11.71 31.97 64.87 124.57 Damage to infrastructure NPR m 0.26 1.52 3.35 9.15 18.57 35.65

Total Direct Cost NPR m 4.10 24.75 54.41 148.59 301.50 578.98

49. The detailed procedure and priorities adopted to define the with-project intervention is not described in this report. Sufficient to say the sub-project proposal is for embankment protection for areas comprising 1,221 ha or 35% of the Biring basin area as shown in Figure 2. In addition, Early Warning Services, shelter housing and non-structural works will be provided. This Final Report includes an estimate of benefits from avoided losses of property and life from Early Warning Systems and Shelter Houses, which will be established at all project sites. These facilities were un-costed in the draft Final Report.

Figure 2: Proposed Flood Mitigation Measures

50. Note that Early Warning and Shelter Houses will primarily benefit the basin population that will not be protected by embankments: this is a significant benefit where the proportion of the unprotected basin population is large. The assumption is that, with-project, the Early Warning System will reduce displacement costs per affected household (located outside embankment protection) by 50% and the mortality and morbidity rates of the same population will be reduced by half. Shelter Houses will reduce the costs of emergency relief (to the households that will later return to damaged houses) by half: this reduction represents a reduction of organization costs (relief will be more aerially focused) and reduced costs of temporary accommodation (whether met by households or government). The population with destroyed houses will still require re-settlement.

51. The known numbers of houses in 1992 within the protected areas were counted and adjusted upwards to obtain an estimate of 2012 house numbers using the growth rates already calculated and indicated in Table 2. This number was subtracted from the estimated total number of houses affected by floods in the Biring basin in 2012 to give the number that will continue to be exposed to flood damage with-project. With-project, about 20% of houses in the Biring basin will be protected (380 out of an estimated 1,923 in 2012). This allows the calculation of incremental flood damage without and with-project within the CBA model.

Table 12: Number and Distribution of Unprotected Houses With-project

Growth rate of Number in Number in Number of houses housing in the protected protected remaining period 1992-2012 area in 1992 area in 2012 unprotected Arjundhara 178% 33 59 144 Chakchaki 64% 34 22 14 Dangibari 127% 46 58 58 Ghailadubba 197% 0 - 606 Gherabari 171% 0 - 24 Rajgadh 138% 59 81 11 Sharanamati 173% 46 80 675 Surunga 191% 0 - 119 Total 174% 218 380 1,543

52. With-project direct infrastructure losses are shown in Table 13. This Table should be compared with Table 11: the difference between the value estimates is the incremental benefit from protecting houses and infrastructure.

Table 13: Summary of With-project Direct Costs by Flood Return Period: Infrastructure

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Damage to housing NPR m 2.73 13.69 30.09 82.17 166.73 320.18 Displacement costs NPR m 0.07 0.32 0.70 1.90 3.86 7.41 Relief and resettlement NPR m 0.92 4.07 8.95 24.44 49.60 95.25 Damage to infrastructure NPR m 0.3 1.2 2.7 7.3 14.9 28.5

Total Direct Cost NPR m 3.99 19.30 42.41 115.84 235.04 451.36

53. The without and with-project incremental direct loss is the difference between the sum of the cost of damage to buildings and infrastructure, displacement costs, emergency relief and resettlement costs without and with-project. Graphing these by flood event as shown in Figure 3, a flood-damage curve results and the difference between the two graphs is the incremental benefit (saved loss) attributed to the project.

Figure 3: Flood Damage Curve Without and With-project - Housing and Infrastructure Direct loss from floods without and with- project: Housing and Infrastructure

700,00 600,00 500,00 400,00 Without project direct loss

300,00 of present infrastructure Predicted 200,00 With project present direct

damage,NPR million loss of present infrastructure 100,00 - 2 5 10 25 50 100 Probability of flood return

54. The incremental benefit from the project is expressed in the CBA calculation as an annual probability of avoided direct loss (APL). The without and with-project loss to events up to 1:100yr is calculated by interpolating the graph between 1:2yr, 1:5yr, 1:10yr, 1:25yr, 1:50yr and 1:100yr and multiplying by the probability of each event occurring. The sum of the loss induced by each event: 1:2yr, 1:5yr, 1:10yr…1:100yr is the annual probability of loss that is constant for each year. The combination of adjustment of the expected loss by the probability of loss occurring in each year, and then discounting that APL in the CBA means that avoided loss is smaller than might be expected.

Indirect Benefit from Increased Infrastructure Development With-project

55. There will be an indirect benefit from increased investment in existing infrastructure in the with-project situation, as the affected population improves the house stock and existing public infrastructure is up-graded as a result of increased security from flood events. With- project, there may also be an increase in the affected population, resulting in the construction of new housing. It was assumed that the flood management intervention would stimulate a 10% increase in new investment in the with-project area (defined as the size of the flood envelope under a without-project 1:100yr flood). This indirect benefit is not flood-dependent: with flood management it will take place over the whole project area. However, new investment is time dependent and it is assumed it takes place incrementally over the whole project life of 25 years.

Indirect Benefit from Increased Infrastructure Development With and Without- project

56. Finally, it is necessary to take into account the change in housing and infrastructure in the basin modeled area over time with-project. The losses and benefits described above are based on assumptions about present infrastructure. Even the direct benefit described in paragraph 55 is about incremental improvement of the present, existing investment. Assuming the project life is 25 years (the benefits from protecting existing infrastructure are unlikely to justify a more expensive, longer-term project) and acknowledging that population in the Terai is growing by about 2% per annum taking into account in-migration (see paragraph 15 for an assessment of the rate of growth in the Biring Priority basin); then it is reasonable to assume that socio-economic conditions will change in time both without and with-project. Population will increase annually, land use will intensify and investment per unit area will increase. That being so, it is reasonable to scale up the direct benefits to the project to take this indirect benefit of protecting as yet undeveloped assets in the future into account. It is assumed that without and with-project direct losses would be 100% greater than present after 25 years in the future. Direct losses are simply recalculated using this factor and processed to calculate annual probability of loss as described in paragraph 54. Then, instead of using a constant value for APL for each year of project life, it can be interpolated between APL in the present and APL in the future.

57. The manipulations described in sections G, H and I above are summarized in Table 14. Comparing this Table with the cost-benefit analysis in Table 32 it should be clearer how APL and indirect benefits are handled in the financial and economic analysis.

Table 14: Processed Without and With-project Direct and Indirect Benefits from Infrastructure

Flood envelopes INCREMENTAL 2 5 10 25 50 100 APL APL 50% 20% 10% 4% 2% 1% Without project direct loss NPR m 4.10 24.75 54.41 148.59 301.50 578.98 55.97 of present infrastructure With project present direct loss of present NPR m 3.99 19.30 42.41 115.84 235.04 451.36 44.74 11.23 infrastructure

Without project direct loss NPR m 8.20 49.51 108.81 297.17 603.00 1,157.9 111.93 of present infrastructure 6 With project present direct loss of present NPR m 7.98 38.59 84.83 231.67 470.09 902.73 89.47 22.46 infrastructure Indirect benefit from Independent of flood NPR m 2.49 6.18 9.23 18.89 34.72 45.52 increased investment events

III. AGRICULTURE

58. The general model calculates the avoided direct loss of crops and the indirect benefit of the flood management project using the same historical and with climate change flood envelopes as described above. Avoided direct loss is the value of the crop lost to flood, indirect benefit is the increase in agricultural economic activity that comes as a result of with-project flood management.

59. Key data is the agricultural area within each flood envelope, which is obtained from the Project GIS output. The data suggest that the area of agriculture in the flood envelopes is large and will be an important source of damage. However, the argument put forward in paragraphs 23 and 24 suggests that the not all the envelope area will be affected. Floods with a frequent return period will affect a relatively small proportion of the area defined in the developed, while floods that return infrequently will affect a much larger proportion. Consistency requires that the affected agricultural area should be adjusted for each flood return period, as was done to calculate infrastructure damage, but the statistical evidence in the historical flood record for doing so is lacking (see Table 41). However, to avoid inflating benefits it was decided to adjust the affected agricultural area downwards in the same proportion as shown in Figure 1:, between the total and affected housing population. Therefore, in the without-project situation, 13% of the agricultural area is assumed to be affected by a 1 in 2 year event, rising to 100% during a 1 in 100 year event.

60. After adjusting the total agricultural area to the affected agricultural area, the cropping pattern and cropping intensity in the affected area must be established. The general model allows for four crops in the cropping pattern. In Biring, paddy rice is the most important crop during the flood period of July to September (see Socio-economic Report). Rice is cultivated under the technologies:

 improved rice with irrigation  improved rice rainfed  traditional rice with irrigation  traditional rice rainfed 61. Crop gross margins have been prepared for each of the four technologies in the without-project, without flood situation and the with-project, without flood situation. They are presented in the Socio-economic Report and summarized in Table 15 and Table 16.

Table 15: Without-project, Without-flood Gross Margins for Paddy Rice Technologies

Table 16: With-project, Without-flood Gross margins for Paddy Rice Technologies

62. A cropping pattern was defined for the project area with reference to District statistics, also described in the Socio-economic report and summarized in Table 17. This provides a basis for weighting the gross margins given in Table 15 and Table 16 to provide a composite gross margin for both the present flood affected project area and the future with-project area, taking into account the expected change in crop technology as a result of flood management.

Table 17: Present Without-project and Expected Future With-project Cropping Pattern

Without-project With-project

Irrigated Rainfed Irrigated Rainfed Improved rice 46% 37% 90% 5% Traditional rice 2% 15% 3% 3%

63. In order to prepare gross margins for flood envelopes, the impact of floods on the baseline gross margins was assessed. The expected yield reduction under specified FHRs was defined taking into account planting date and growth stage of the crop. The expected yield reduction is shown in Table 18. The Table specifies that floods during July will affect the crop at recently planted stage and cause a considerable yield reduction. A flood from mid-August through to the end of September will cause comparatively little damage. Luckily, the dates of floods are known for Biring (see Table 41) and these were used to weight the anticipated yield reduction by growth stage to a basin-specific yield loss.

Table 18: Expected Loss of Yield of Rice Depending on Flood Date

Flood Weighted expected yield July 1st Aug 1 to Aug 15 to Hazard loss as function of FHR to 31st Aug 15 Sept 30 Rating and historical flood dates

0 0% 0% 0% 0% 0.5 60% 20% 10% 35% 0.6 60% 20% 10% 35% 0.7 60% 20% 10% 35% 0.8 60% 20% 10% 35% 0.9 70% 20% 10% 39% 1.0 70% 20% 10% 39% 1.1 70% 20% 10% 39% 1.2 70% 20% 10% 39% 1.3 70% 20% 10% 39% 1.4 70% 20% 10% 39% 1.5 70% 20% 10% 39% 1.6 70% 20% 10% 39% 1.7 70% 20% 10% 39% 1.8 100% 80% 40% 74% 1.9 100% 80% 40% 74% 2.0 100% 80% 40% 74% 2.1 100% 80% 40% 74% 2.2 100% 80% 40% 74% 2.3 100% 80% 40% 74% 2.4 100% 80% 40% 74% 2.5 100% 80% 40% 74%

Flood Weighted expected yield July 1st Aug 1 to Aug 15 to Hazard loss as function of FHR to 31st Aug 15 Sept 30 Rating and historical flood dates

2.6 100% 100% 100% 100% 2.7 100% 100% 100% 100% 2.8 100% 100% 100% 100% 2.9 100% 100% 100% 100% 3.0 100% 100% 100% 100% 3.1 100% 100% 100% 100% 3.2 100% 100% 100% 100% 3.3 100% 100% 100% 100% 3.4 100% 100% 100% 100% 3.5 100% 100% 100% 100%

64. The FHR for each flood envelope was defined, based on the proportion of the agricultural area affected by low, moderate, significant and extreme ratings. Note that to establish agricultural losses, the weighting is done by area. To establish damage to housing, the weighting was done by number of buildings affected by FHR (see paragraph 38).

65. Then, the FHR of each flood envelope was used to specify the expected yield reduction, using Table 18 as an Excel LOOKUP table. Having defined the yield reduction, the gross margins then were revised for the four crop technologies under flood conditions of 1:2yr, 1:5yr, 1:20yr, 1:50yr and 1:100yr depending on the (weighted by area) FHR for each envelope.

66. The issue of loss of both crop value and loss of inputs is treated as follows. Yield cannot be negative, but if it is zero the crop will not be harvested, so variable costs will be incurred only up to the date of flood. This is simulated by a calculation of pre-flood variable costs that exclude the cost of harvesting. In the case of zero yield, gross margin will be negative to the value of the variable costs incurred up to the point of flooding.

67. It is also necessary to do a similar calculation of crop loss for the with-project situation. The proposed with-project intervention shown in Figure 2 will protect 1,156 ha of agriculture at a return period of 1:50yr with climate change. The balance of the agricultural area in the basin will continue to be flood-affected. The CBA model adds the value of production from both areas and subtracts the value of the production in the without project situation to calculate the net benefit with-project.

68. The gross margins show that a small increase in the use of inputs is expected in the protected area with-project: farmers will intensify production if they are more confident that flood damage will be reduced by flood management. It is also assumed there will be some intensification of the cropping pattern, with a greater area put to improved rice and irrigation; see Table 17. The Socio-economic Report gives details on the assumptions made. With- project changes in gross margins and cropping pattern are important because intensification will be an indirect benefit of flood management.

69. Note that with-project, with-project gross margins apply only to the protected area and that within that area no flood damage is assumed. With-project, without-project gross margins continue to be applied on the whole unprotected area and within that, on the flood affected area, the without-project gross margins with floods are applied.

70. The weighted gross margin and production per ha for the affected agricultural area (see paragraph 59) without and with-project are shown in Table 19. On the with-project protected area, the gross margins and production per ha shown in Table 16 are applied, weighted by the proportion of technologies shown in Table 17. The gross margin is estimated as NPR 19,300 per ha and the production is estimated to be 4.3 tons per ha. On the with-project area outside the protected area but unaffected by floods (in a year without flooding) the gross margins and production shown in Table 15 are applied, again weighted by the proportion of technologies shown in Table 17. The gross margin is estimated as NPR 16,530 per ha and the production is estimated to be 3.7 tons per ha.

Table 19: Without-project Gross Margin and Production on Flood Affected Land Without and With-Project by Flood Return Period

Without- Flood envelopes project 2 5 10 25 50 100 cropping pattern 50% 20% 10% 4% 2% 1%

Gross margin, NPR per ha Improved irrigated 46% 18,096 -47,979 -47,979 -47,979 -47,979 -47,979 Improved rainfed 37% 16,438 -41,268 -41,268 -41,268 -41,268 -41,268 Traditional irrigated 2% 13,814 -43,772 -43,772 -43,772 -43,762 -43,772 Traditional rainfed 15% 11,647 -36,745 -36,745 -36,745 -36,745 -36,745 Weighted gross margin, 16,430 -43,727 -43,727 -43,727 -43,726 -43,727 NPR/ha kg of crop per ha Improved irrigated 46% 4,073 1,076 1,076 1,076 1,076 1,076 Improved rainfed 37% 3,557 940 940 940 940 940 Traditional irrigated 2% 3,542 936 936 936 936 936 Traditional rainfed 15% 2,976 787 787 787 787 787 Weighted production, 3,707 980 980 980 980 980 kg/ha

71. The appropriate gross margin per ha and production per ha can then by multiplied by affected and unaffected agricultural area in each without and with-project flood envelope and in the with-project situation the production from the protected area is added. By subtracting gross margin for each flood return period in the without and with-project situation the incremental direct loss can be calculated. By subtracting gross margin without-project from gross margin with-project for each flood envelope the indirect benefit of increased cropping intensity with-project can also be estimated. The without and with-project direct losses and indirect benefit are summarized in Table 20 and Table 21.

Table 20: Summary of Without-project Direct Costs by Flood Return Period: Agriculture

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Total Agriculture area ha 781 2,142 2,243 2,464 2,595 2,699 Agricultural area affected ha 99 363 551 1,110 2,039 2,699 Protected area ha ------Gross margin without flood NPR m 12.84 35.19 36.85 40.48 42.63 44.34 Gross margin with flood NPR m 12.84 13.33 3.72 -26.30 -80.00 -118.00 Total direct loss with flood NPR m - 21.85 33.14 66.79 122.64 162.33 Total indirect benefit with flood NPR m ------Production without flood tons 2,896 7,939 8,315 9,134 9,620 10,003 Production with flood tons 2,896 6,948 6,813 6,106 4,060 2,644 Change in production tons - -991 -1,502 -3,028 -5,560 -7,360

Table 21: Summary of With-project Direct Costs and Indirect Benefits by Flood Return Period: Agriculture

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Total Agriculture area ha 609 1,151 1,213 1,352 1,439 1,523 Agricultural area affected ha 77 195 298 609 1,130 1,523 Protected area ha 172 990 1,030 1,112 1,156 1,176 Gross margin without flood NPR m 13.33 38.03 39.81 43.68 45.95 47.71 Gross margin with flood NPR m 13.33 26.28 21.89 7.04 -22.04 -43.90 Total direct loss with flood NPR m - 11.75 17.92 36.64 67.99 91.61 Total indirect benefit with flood NPR m 0.50 2.84 2.96 3.19 3.32 3.38 Production without flood tons 3,369 9,234 9,671 10,624 11,188 11,635 Production with flood tons 3,369 8,614 8,726 8,692 7,603 6,804 Change in production tons - -619 -945 -1,932 -3,585 -4,831

72. Direct losses can be graphed on the standard flood damage curve, as shown in Figure 4.

73. The losses under each flood event are then weighted by the probability of the event occurring, the sum of the damages being the annual probability of loss (APL). A small refinement can improve APL. Bearing in mind that APL is calculated on the basis of the present cropping pattern and technology, it can be inflated slightly as cropping intensity increases and technology improves over the life of the project. See paragraph 56 above.

74. Indirect benefit quantifies the difference in value of production between different (future without and future with-project) technologies. It is not dependent on flood probability and is applicable to the whole of the affected area.

Figure 4: Flood Damage Curve Without and With-project: Agriculture Direct Loss from Floods Without and With- project: Agriculture

180,00 160,00 140,00 120,00 100,00 Without project direct loss of

80,00 present production Predicted 60,00 With project direct loss of present production direct loss, directloss, NPR million 40,00 20,00 - 2 5 10 25 50 100 Probability of flood return

75. This Final Report includes an estimate of avoided losses with-project from periodic floods that cause total land destruction by erosion and reduction of land quality by sedimentation. This estimate is included at the request of DWIDP. The historic flood record does not give sufficient information to link loss of land and sedimentation of land with floods of different return periods (see Table 41, penultimate column: the data is nearly always missing). Nevertheless, it is common knowledge that such losses from floods occur regularly and it is reasonable to make an estimate of resulting losses. DWIDP have asked the Consultant to take into account a total area affected over 25 years of 10 ha at Biring, of which 50% is totally lost and 50% affected by sedimentation. DWIDP estimates an annual loss of NPR 0.25 million: this has been incorporated into the CBA as a direct annual loss, which is avoided with-project.

76. The manipulations described in the paragraphs above are summarized in Table 22. Comparing this Table with the cost-benefit analysis in Table 32 it should be clearer how APL and indirect benefits are handled in the financial and economic analysis.

Table 22: Processed Without and With-project Direct and Indirect Benefits from Agriculture

Flood envelopes

2 5 10 25 50 100 APL INCREMENTAL APL

50% 20% 10% 4% 2% 1% Without project direct loss of present production NPR m - 21.85 33.14 66.79 122.64 162.33 30.49 With project direct loss of present production NPR m - 11.75 17.92 36.64 67.99 91.61 16.55 13.94 Without project direct loss of present and future production NPR m - 43.70 66.27 133.58 245.28 324.67 60.98 With project direct loss of present and future production NPR m - 23.49 35.84 73.28 135.99 183.23 33.09 27.88 With project indirect net benefit from crop Independent of flood events, NPR m 0.50 2.84 2.96 3.19 3.32 3.38 intensification and crop area expansion dependent on cropped area

IV. MORTALITY AND MORBIDITY

77. The historical flood record includes reports of loss of life, missing persons and injuries. Loss of life and missing persons as a result of flooding is probably more consistently reported than destruction and damage to property. Reports of injury in the same record are rarer; probably floods cause more psychological damage (which is unreported) than physical injury.

78. It is difficult to demonstrate any relationship between flood return period and the number of deaths reported. This is because flood deaths frequently occur as a result of “risk- taking” behavior. Risk-taking is an individual situation uncorrelated with loss of property, the total numbers of people affected by a flood and the return period of the flood.

79. The parameters mortality and morbidity are expressed as a percentage of a population – the population has to be known before a death or injury rate can be calculated. Thus flood events associated with deaths but with no reported affected population are useless for analysis of mortality. But this can be turned into an advantage for analysis because this set of data includes risk-taking behavior, for example misjudged attempts to ford rivers in spate.

80. A data set of 505 flood records for the period 1991-2015 that included an estimate of the affected population was available from the Basin Ranking Study previously carried out under this project. A composite measure of magnitude was calculated, sufficient to classify floods as low, high, very high and extreme in damage impact, but no flood return periods were available. 95 records included reports of death, missing persons or injury. Summing these reports by damage magnitude gave an estimate of mortality, mortality and missing persons, and mortalities, missing persons and morbidity, as shown in Table 23.

Table 23: Estimate of Mortality and Morbidity by Magnitude of Flood Damage

Mortality, Magnitude Mortality Affected Dead Missing Injured missing of flood Events Mortality & persons People People People and damage missing Morbidity Low 373 222,110 43 9 26 0.019% 0.023% 0.035% High 91 233,618 55 14 6 0.024% 0.030% 0.032% Very high 32 425,852 170 9 50 0.040% 0.042% 0.054% Extreme 8 154,933 125 7 37 0.081% 0.085% 0.109%

81. The results are intuitively reasonable, with all three rates increasing with flood damage magnitude. The rates can be expressed as incidents per 10,000 people, which is slightly easier to visualize, and plotted on a graph, see Figure 5. There are very few records of extreme events and it is not known if any of them represent a flood of 1:100yr return period. Assuming extreme events of flood damage are related to 1:50yr events, the graph can be interpolated for an estimate of the number of deaths, missing persons and injuries expected in the range of return periods that are of interest to the CBA. But the approach is not rigorous because rates are only implicitly linked to flood return periods.

Figure 5: Mortality and Morbidity Rates by Magnitude of Flood Damage

82. A total analysis of river basins (and of those, only the modeled, lower areas) is described above, which gives no indication of the relative risk of death or injury from floods between basins. An indication of this is given in Table 24, which shows that East Rapti and Narayani have high historical numbers of dead or injured people per 10,000 of the present total population. But the proportion of these who are actually flood affected is not known (it is likely to much smaller than the 2011 total population), so there is no evidence to hand to justify adjusting the rates calculated in Table 23 to reflect different mortality rates between basins.

Table 24: Numbers of Dead, Missing and Injured by Modelled Basin, 1991-2015

Modelled basin Dead Missing Injured Total 2011 population Affected per 10,000 Aurahi 3 2 - 5 160,141 0.312 Bakraha 1 - - 1 140,450 0.071 Balan 3 - - 3 146,598 0.205 Banganga - - - - 183,690 - Biring 8 5 - 13 133,313 0.975 Budhi - - - - 285,219 - Chaudhar 17 - - 17 95,216 1.785 Chisang 1 - 1 2 141,650 0.141 Dodha 4 - - 4 139,851 0.286 E-Rapti 139 21 100 260 763,318 3.406 Gagan 2 - 12 14 110,596 1.266 Ialbakeya 43 - 1 44 357,479 1.231 Jalad - - - - 194,498 - Jhim 6 - - 6 91,285 0.657 Kamal 1 - - 1 37,946 0.264 Kandra - - - - 57,236 - Kankaii 6 - - 6 56,831 1.056 Karnali 10 11 - 21 196,681 1.068

Modelled basin Dead Missing Injured Total 2011 population Affected per 10,000 Khando - - - - 88,009 - Khutiya - - - - 51,250 - Lakhandehi 28 - - 28 190,174 1.472 Mohana 4 - - 4 166,675 0.240 Narayani 114 - 5 119 533,619 2.230 Ratuwa 1 - - 1 161,013 0.062 W-Rapti 2 - - 2 292,112 0.068

83. Having made an estimate of likely dead, missing and injured per 10,000 flood affected people it is easy enough to apply these rates to the flood affected population as defined in paragraph 41 - 45, taking care to disaggregate the rates and specify dead, missing and injured separately. Combined, the statistic is referred to as “casualties”. A flood damage curve can be prepared as shown in Figure 6.

Figure 6: Flood Damage Curve Without and With-project: Casualties

Flood Direct Damage: Casualties 20,00

15,00

10,00 Without project casulaties

5,00 With project casualties missing missing andinjured -

Totalnumber ofpeople killed, 2 5 10 25 50 100 Flood return period

84. The same data can be used to establish an annual probability of casualties without and with-project using the Annual Probability of Loss method described in paragraph 54. The difference between the APL without and with project basically represents the number of casualties the project expects to avert per year of its existence. There is no need to express this in monetary terms; it can stand alone as a project indicator, along with NPV, IRR etc.

Table 25: Annual Probability of Casualties Saved

Without-project pa With-project pa Saved pa attributable to project

Lives saved 0.77 0.33 0.44 Injuries saved 0.10 0.04 0.06 Missing saved 0.27 0.12 0.15 Casualties 1.14 0.49 0.65

V. LIVESTOCK

85. The same procedure as described for quantifying the probability of mortality and morbidity was used to estimate the probability of the death of livestock during a flood event. There is no problem about allowing for risk-taking behavior by livestock; but the main problem remained, which was how to establish the “affected” livestock population to allow a mortality rate to be calculated. This was complicated by the fact that the historical flood record specifies only “livestock” deaths. There is obviously a huge difference in value if each livestock unit represented one head of cattle rather than one head of poultry. Further, it is known that poultry suffer disproportionately compared to other livestock types from flood events.

86. The procedure used required estimating the livestock herd of each flood-affected household. This was assisted by the study Poverty, Livestock and Household Typologies in Nepal, Maltsoglou and Taniguchi, PPLPI Working Paper 13, FAO 2004, though the Consultants modified the estimates therein to be relevant to specific Priority Basins. The study notes that the statistics for the period show that 86% of households in the Terai are livestock owners and gives sufficient additional information to show that the mean household herd size is 2.2 Tropical Livestock Units (TLU) composed of 8% cattle (2 head), 8% sheep and goats (two head), 8% pigs (two head) and 77% chickens (20 head). Bearing in mind that one TLU must then equal 11.82 head of the household livestock herd, this is sufficient to calculate the affected population of animals by type, against which “livestock” deaths can be disaggregated and measured. The disadvantage of the method is that it does not take into account bias in livestock death: as mentioned above, chickens die in floods more frequently than other domestic animals.

87. Figure 7 shows that “livestock” mortality in flood events can be deduced from the MOHA/DWIDP database as about 300 head per 10,000 for extreme events. The calculation for deriving this statistic required, for each reported flood:

 adjusting the affected households to derive the number of affected households owning livestock

 deriving the numbers of affected households owning cattle, shoats, pigs and poultry

 disaggregating livestock deaths according to the composition of the household herd

 expressing reported mortality per 10,000 head by livestock type for reported floods of low, high, very high and extreme events as shown in Figure 7.

Figure 7: Livestock Mortality Rates by Magnitude of Flood Damage

88. Returning to the general model, it was then straightforward to estimate the affected livestock herd in both TLU and number of head (owned by the affected population in the Priority Basin and expressed as households) and calculate expected fatalities for each flood envelope by applying the mortality rates shown in Figure 5.

89. These fatalities could then be valued because the composition of the household’s herd had been estimated (see paragraph 86). Multiplying by price per head of family herd by type and summing gives a value of about NPR 10,000 per head. The higher value of poultry in the household herd is given its weight here, though there is still no way to account for bias in mortality between types of stock. A flood damage curve was established, as shown in Figure 8.

Figure 8: Flood Damage Curve Without and With-project: Direct Loss of Livestock Value

Direct Loss from Floods Without and With project: Livestock 30,00 25,00 20,00 15,00 Without-project livestock loss 10,00 With-project livestock loss 5,00 - 2 5 10 25 50 100

Flood return period Value of Livestock Livestock of Value loss, NPR million

90. The annual probability of loss was estimated as shown in Table 26. This was disappointingly low after so many convoluted calculations, but likely to be realistic when one considers that most livestock deaths from flood events are probably of chickens. No adjustment for anticipated indirect benefits was made – households are unlikely to improve or invest in more livestock as a result of improved flood management.

Table 26: Processed Without and With-project Indirect Loss from Livestock

Flood envelopes INCRE- 2 5 10 25 50 100 APL MENTAL APL 50% 20% 10% 4% 2% 1% Without- project NPR m - 0.04 0.70 1.76 12.31 25.93 0.87 livestock loss 0.17 With- project NPR m - 0.04 0.56 1.41 9.84 20.74 0.69 livestock loss

VI. FLOOD PREVENTION AND COPING COSTS

91. Residents living in flood risk areas incur flood damage prevention costs before flood events. Prevention costs cover flood proofing of houses, bank stabilization by tree and grass planting and maintenance, and establishment and maintenance of live fencing to check floods. The measures taken may be individual or cooperative. In the without-project situation it is assumed that between five and ten person days per annum are spent per (affected) house in flood proofing and a further 50 days per hectare in “built up” areas (as specified on the land use map) carrying out bank stabilization and live fencing. There will be minor material costs but only labor has been costed.

92. Costs of coping after flood events are much more significant. These costs relate to house repair after flood damage. Although the value lost by flood damage has already been estimated as an infrastructure cost, an additional cost is incurred by the owner in making good that damage: the situation is analogous to the negative gross margin incurred as a result of losing both the crop and the inputs required to grow that crop up to the point of destruction. Only damaged houses are repaired, destroyed houses are replaced through re-settlement measures already costed (see paragraph 44. Repairing damage is assumed to cost about 10% of depreciated house value for a house sustaining <25% damage and 25% of depreciated house value for a house sustaining >25% damage.

93. Repairing in-field infrastructure after a flood event is a significant cost to be added to the loss of crop production and operating costs. Paddy bunds must be re-made (40 person days per ha), water points for both irrigation and water supply repaired (ten days per ha), cattle sheds re-constructed (ten days per unit) and areas affected by sedimentation reclaimed (200 days per ha). By far the greatest cost is the repair of paddy bunds and land reclamation.

94. The cost of labor is the opportunity cost of paddy cultivation without flood impact as calculated in the gross margin (see Table 15). This is quite high, but reasonable if most households are owner-occupiers of land rather than hired laborers.

95. Indirect coping costs are also incurred within post-flood food markets. It is well known that the price of staples rises in flood-affected areas as crop losses are substituted for by food purchases; demand for food rises and supply falls. The situation can be analyzed with reference to The Food Balance Sheet for Nepal, FAO 2011, available from FAOStat. Table 27 shows the average food supply consumed per person per year in 2011, which provided 2,530 kcals per capita per day. This diet would cost about NPR 170 per capita per day in 2015 prices if all food were purchased in markets reported in the Agricultural Yearbook 2010/11.

Table 27: Summarised Food Budget Per Capita

Food Rs/kg Food Cost % of Rs/kg Food supply Food supply (2010/11), supply of food 2015 '000 quantity (kcal/capita/ Agric quantity daily sup- (inflated tons (kg/capita day) Yrbook (kg/capita diet ply with CPI) Item /yr) Tab 9.1 /day) 2015 Cereals 5,066 186.6 33% 1609 35 49 0.51 25 Roots 2,176 80.1 14% 150 100 139 0.22 30 Sugar 961 35.4 6% 28 83 116 0.10 11 Pulses 288 10.6 2% 92 104 145 0.03 4 Nuts 53 2.0 0% 13 - 0.01 - Oilseeds 20 0.7 0% 5 69 96 0.00 0 Vegetable oils 273 10.1 2% 243 133 185 0.03 5 Vegetables 3,048 112.2 20% 74 110 153 0.31 47 Fruit 1,460 53.8 10% 65 120 167 0.15 25 Stimulants 11 0.4 0% 1 - 0.00 - Spices 167 6.2 1% 57 - 0.02 - Beverages 48 1.8 0% 4 - 0.00 - Meat 330 12.2 2% 44 225 313 0.03 10 Butter/ghee 39 1.4 0% 34 456 634 0.00 2 Eggs (kg) 31 1.1 0% 4 155 216 0.00 1 Milk (kg) 1,348 49.6 9% 103 40 55 0.14 7 564.2 2,526 169

96. This estimate can be used to calculate the amount of food stuff required per capita per year in crop equivalent, allowing for processing, feed, seed and waste. The figure of most interest is the annual requirement of 315 kg of cereal per person; in the study area most of this will be obtained as paddy. The amount of paddy produced with flood is known (see Table 20) as well as the displaced population (see Table 10). The incremental demand for paddy by the displaced will increase the price of paddy depending on the price elasticity of demand, which for a staple food is very low. One could imagine that for every 1% increase in demand price will increase by 1%. The paddy price will therefore increase in direct ratio to the increase in demand as a result of a flood event. This might be only 1-2% for floods with a return period of 1:2yr and 1:5yr, but for a 1:50yr prices might rise by 15%. Whatever the price increase, it must be borne by the flood-affected population. A similar calculation could be done for the supply and demand of other food crops if they were grown in the flood-affected area.

Table 28: Estimate of Crop Production Required to meet Annual Dietary requirement

Amount Kg of raw required product Item for per Adjustment Ratio per annum annum kg per capita per capita Cereals 187 paddy milling ratio 169% 315.4 Roots 80 feed/seed/waste 129% 103.3 Sugar 35 8 kg gur per 100kg cane 8% 442.5 Pulses 11 feed/seed/waste 129% 13.7 Nuts 2 feed/seed/waste 129% 2.6 Oilseeds 1 feed/seed/waste 129% 0.9 Vegetable oils 10 35 kg from 100 kg seed 286% 28.9 Vegetables 112 feed/seed/waste 129% 144.7 Fruit 54 feed/seed/waste 129% 69.4 Stimulants 0 - Spices 6 - Beverages 2 - Meat 12 waste and by products 129% 15.7 Butter/ghee 1 waste and by products 129% 1.8 Eggs (kg) 1 Waste 129% 1.4 Milk (kg) 50 Waste 129% 64.0

97. A summary of flood prevention and coping costs without and with project is given in Table 29 and Table 30 and expressed on a flood damage curve in Figure 9.

Table 29: Without-project Direct and Indirect Costs of Flood Prevention and Coping

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Costs of flood prevention NPR m 0.11 0.27 0.40 0.81 1.49 1.95 Costs of flood coping Repair of housing NPR m 0.49 2.70 5.93 16.20 32.88 63.14 Repair of paddy bunds and field structures NPR m 0.44 1.60 2.42 4.89 8.97 11.88 Repair of water points NPR m 0.11 0.27 0.40 0.82 1.51 1.98 Repair of cattle sheds NPR m 0.01 0.03 0.07 0.20 0.40 0.77 Repair of miscellaneous NPR m 0.01 0.03 0.07 0.20 0.40 0.77 Areas affected by sedimentation NPR m 0.00 0.01 0.01 0.02 0.03 0.04 Reclamation of sedimentation areas NPR m 0.22 0.80 1.21 2.44 4.49 5.94 Increased expenditure in food markets NPR m 0.00 0.02 0.08 0.63 3.92 17.55 Total NPR m 1.38 5.72 10.60 26.22 54.10 104.03

Table 30: With-project Direct and Indirect Costs of Flood Prevention and Coping

Flood with Return Period Item Unit 50% 20% 10% 4% 2% 1% Costs of flood prevention NPR m 0.11 0.21 0.32 0.65 1.19 1.56 Costs of flood coping Repair of housing NPR m 0.49 2.16 4.75 12.96 26.30 50.51 Repair of paddy bunds and field structures NPR m 0.34 0.86 1.31 2.68 4.98 6.70 Repair of water points NPR m 0.11 0.22 0.32 0.66 1.21 1.59 Repair of cattle sheds NPR m 0.01 0.03 0.06 0.16 0.32 0.62 Repair of miscellaneous NPR m 0.01 0.03 0.06 0.16 0.32 0.62 Areas affected by sedimentation NPR m 0.00 0.00 0.00 0.01 0.02 0.02 Reclamation of sedimentation areas NPR m 0.17 0.43 0.66 1.34 2.49 3.35 Increased expenditure in food markets NPR m 0.00 0.01 0.04 0.28 1.34 5.52 Total NPR m 1.23 3.94 7.51 18.90 38.17 70.49

Figure 9: Flood Damage Curve Prevention and Coping Costs

Direct and Indirect Losses from Floods With and Without-project: Prevention and Coping 140,00

120,00

100,00

80,00 Without project cost of present prevention and coping 60,00 With project cost of present coping and prevention 40,00

20,00

Total number of people killed, missing number missing Total injuredkilled, people of and - 2 5 10 25 50 100 Flood return period

98. All these costs are flood dependent and the annual probability of loss must be calculated in the usual way (see paragraph 54) and an adjustment made for the expected increased economic activity over time as the population increases (see paragraph 56). A summary of the input to the cost-benefit analysis (see Table 32) is given in Table 31.

Table 31: Processed Without and With-project Direct and Indirect Costs: Prevention and Coping

Flood envelopes

2 5 10 25 50 100 INCREMENTAL 50% 20% 10% 4% 2% 1% APL APL Without project cost of present NPR m 1.38 5.72 10.60 26.22 54.10 104.03 11.78 prevention and coping With project cost of present NPR m 1.2 3.9 7.5 18.9 38.2 70.5 8.53 3.26 coping and prevention Without project cost of present and future NPR m 2.75 11.44 21.20 52.43 108.19 208.06 23.57 prevention and coping With project cost of present and NPR m 2.5 7.9 15.0 37.8 76.3 141.0 17.05 6.52 future coping and prevention

This Final Report includes an estimate of benefits with-project from riverside plantations for timber and firewood. This project component was not considered at draft final stage and is now included at the request of DWIDP. DWIDP have asked the Consultant to assume riverside plantations of 100 ha at each of the six project sites. DWIDP estimates an annual benefit of NPR 20,000 per annum per ha or NPR 2 million per annum. The Consultants have used the DWIDP benefit estimate and allowed for nursery, planting and maintenance cost and the development of woody biomass yield over the life of the project. The benefit stream from riverside plantations has been incorporated into the CBA as a direct annual benefit.

VII. CBA WITH IMPACT OF CLIMATE CHANGE

CBA in Financial Prices

99. The ToR require that the impact of climate change on flood management investment proposals be evaluated. This requires estimating equivalent probabilities of future floods from the probability of historical floods and substituting them in the APL calculation. A suitable equation to relate the two probability curves has been defined in the hydrological studies for this Basin as:

CC Return period = 0.494 (Historical Return period)^1.108

100. As a result of applying this equation to historical return periods, floods with defined past probability will be estimated to occur more frequently in the future. There is however an issue with curve-fitting for floods with a higher probability than 50%, which are expected to recur less frequently. This CBA, which is concerned with estimating the impact of floods with a lower probability than 50%, ignores this anomaly.

101. Information is also given from the hydrological studies on the characteristics of floods that will occur in the future with the probability of 1 in 2 year, 1 in 5 year…etc. Floods with defined past probability will be estimated to occur more frequently in the future but they will also be more aggressive. This analysis therefore must: (i) substitute the new probabilities of floods with climate change into the APL equation (as described in paragraph 54) and (ii) revise direct losses (both without and with-project) on the basis of the new flood envelope characteristics for future floods with climate change. The general CBA model has two switches to do this: one to activate the new probabilities and one to activate the new flood characteristics. Combining increased probability of floods with increased flood aggression leads to the CBA results shown in Table 32Table 32and Table 36 in financial and economic prices respectively.

102. Note that taking into account the increased aggression and periodicity of floods with climate change, the casualty rate increases by 125%. This is because of the increased population affected by floods (vulnerability) as well as the increased area, aggression and periodicity (risk) of floods.

103. It is also necessary to review future crop yields with-climate change; this has not been done and should be a matter for the feasibility study.

104. Incremental avoided direct losses (without-project and with-project) and indirect project benefits with the investment and maintenance cost are compiled in Table 32 which shows the CBA using the benefits calculated in the preceding sections matched to the costs of the proposed flood management project (NPR 1,074.28 m). Prices are in constant 2015 financial NPR million.

105. The length of embankment proposed at Biring is 23.2 km and it has been assumed this will be constructed over a period of two years and expected to have a life of 25 years. Scheduling of investment costs and benefits is important, given the heavy discounting of future benefits by (i) adjusting for APL and (ii) discounting with the assumed social discount rate of 10%. Up-front costs will be given a higher weighting than downstream benefits.

106. The Management, Operation and Maintenance (MOM) cost is assumed to be 1% of investment costs, following DWIDP direction. In practice MOM would be carried out on an “as needed” basis.

107. The embankment is designed to protect 1,220.6 ha from the impact of a 1:50 year flood taking into account climate change. In practice engineers consider that the freeboard allowance would protect a greater area from a 1:100 year flood taking into account climate change. It follows that the area subjected to a historical flood of the same magnitude would also be protected. For this reason benefits include protection of the area up to the extent of the 1:100 year flood envelope for both historical floods and predicted floods taking into account climate change.

108. It is interesting to look at the composition of benefits. Indirect benefits from infrastructure protection are similar in magnitude to avoided direct losses. In addition some of the benefits of coping strategies are indirect, even though they are linked to magnitude of flood, e.g. costs arising from increased food prices. This is intuitively reasonable in an area with few assets to speak of and some development potential.

109. Benefits from avoided casualties are expressed as persons “saved”, which is more transparent (and more methodologically and philosophically defendable) than expressing saved casualties in value terms. Biring is a relatively large project but the housing and population density is rather low. The sub-project is only expected to save about 15 casualties (most of which would be fatalities) during the 25 year life of the project.

110. Trans-boundary issues (i.e. impacts on the other side of the international border with India below the basin modeled area) are not included in the CBA. This should be handled at feasibility level.

Table 32: Cost:Benefit Analysis –Floods with Climate Change: Constant 2015 Financial Prices, NPR million

CBA in Economic Prices

111. The shadow exchange rate (SER) is the economic price of foreign currency and shadow exchange rate factor (SERF) is the conversion rate of officially valued currency to economically valued currency. Both are used in economic cost benefit analysis to re-value traded goods to a commensurate economic value with non-traded goods (shadow exchange rate approach, as used in this analysis) or to estimate the closely related standard conversion factor (SCF) which is used to adjust the financial value of non-traded goods (the conversion factor approach) to value them equivalently to traded goods. Either method eliminates the effect of the premium paid on traded goods over non-traded goods through national trade policy, where imports are normally taxed and exports are sometimes (though rarely) subsidised. Table 33 shows these estimates for Nepal for the period 2009/10 - 2014/15.

112. The calculation rests on identifying the proportion of import duties and tariffs as a percentage of imports. Exports are treated similarly. Trade elasticities are used to perform the final part of the calculation and provide weights for the relative importance of imports and exports depending on consumer preference in the economy. Trade elasticities are derived from macro-economic studies: a reference is quoted.

113. The result for 2014/15 is a SCF of 0.92, a SERF of 1.09 and a SER of NRP 107 per US$. There is no obvious change in the estimates of SERF and SCF during the last five years.

Table 33: Estimate of SER, SERF and SCF for Nepal, 2010-2015

Items Variables/Equations Source Comments Unit 2010/11 2011/12 2012/13 2013/14 2014/15 CIF, converted from Total imports M 1/ Nepal Foreign Trade Statistics 2071/72 million US$ at OER 390,300 506,700 613,600 716,100 786,200 Special transactions SM no data million Other nonresponsive imports NM negligible million

Net Imports dM=M-SM-NM million 390,300 506,700 613,600 716,100 786,200 FOB, converted from Total exports X Nepal Foreign Trade Statistics 2071/72 million 62,700 72,100 77,400 89,600 85,200 US$ at OER Special transactions SX no data million Reexports RX no data million Other nonresponsive goods NX no data million Net exports dX=X-SX-RX-NX million 62,700 72,100 77,400 89,600 85,200

Trade deficit dQ=dM-dX million 327,600 434,600 536,200 626,500 701,000 Import tariffs IT Budget Speeches (FY 2014/15 to 2001/02), MinFin million 34,314 40,906 54,328 64,122 70,487 Net tariff equivalent of Quantitative TR not applicable: zero million Restrictions (QR) Import tariff rate tm=(IT+TR)/dM 8.8% 8.1% 8.9% 9.0% 9.0% Export taxes XT Budget Speeches (FY 2014/15 to 2001/02), MinFin million 1,399 2,485 2,604 3,859 5,718 Net tax equivalent of QRs XX not applicable: zero million Export subsidies XS not applicable: zero million Export tax rate tx=(XT+XX-XS)/dX 0.4% 0.6% 0.5% 0.6% 0.8% Elasticity of supply (exports) es Tokarick (2010) 2/ 0.52 0.52 0.52 0.52 0.52 Elasticity of demand (imports) ed Tokarick (2010) 2/ -1.23 -1.23 -1.23 -1.23 -1.23 Weight on supply Ws=es/[es-{ed*(dM/dX}] 0.0636 0.0567 0.0506 0.0502 0.0438 Wd=-{ed*(dM/dX)} / [es - Weight on demand 0.9364 0.9433 0.9494 0.9498 0.9562 {ed*(dM/dX)}] Official exchange rate OER IMF3/ local /US$ 75 72 81 88 98 Using Official Exchange Rate SER = Ws*OER*(1-tx) + Shadow exchange rate local /US$ 81 77 88 95 107 Wd*OER*(1+tm) Shadow exchange rate factor SERF = SER/OER 1.08 1.08 1.08 1.08 1.09 Standard conversion factor SCF = OER/SER 0.92 0.93 0.92 0.92 0.92 Sources

1/ http://www.customs.gov.np/en/annual.html 2/ Country calculations from A Method for Calculating Export Supply and Import Demand Elasticities, Stephen Tokarick, IMF Working Paper 2010 3/ International Monetary Fund, World Economic Outlook Database, April 2015

114. The recent ADB PPTA Innovation for More Food with Less Water (ADB TA 7967-REG) estimated a Shadow Wage Rate Factor (SWRF) of 0.8. The SWRF is intended to account for the opportunity cost of labour and was adopted in this study without criticism, though local variation can be expected in the SWRF depending on in and out-migration; at feasibility stage a suitable estimate would have to be made for each Priority Basin.

115. An estimate for a conversion factor for investment costs for flood protection works was calculated in detail at Draft Final Report stage but subsequently the cost estimate changed considerably. The latest estimate continues to suggest that, after increasing foreign costs by the SERF to allow for the foreign exchange premium, eliminating transfer costs and price contingencies, deducting taxes and adjusting by the SWRF, the economic cost of flood protection is lower than its financial cost. Note that the economic cost estimate does not include land compensation and resettlement costs. These are transfer costs and are not to be included in the economic price. The conversion factor for civil works at Biring is about 0.86. A conversion factor for maintenance has been estimated separately.

116. A conversion factor was not available for housing and public infrastructure. Probably much house construction is done exclusive of tax, the unskilled labour component is high and mostly local materials (stone, brick, bamboo and wood) are used in construction. A conversion factor of 1 was used. The factor chosen is in fact important to the value of economic benefits from the project, given that a substantial proportion of avoided losses with-project relate to local housing. Conversion factors for public infrastructure are also estimates.

117. Economic valuation of rice cultivation was done in the study Building Climate Resilience of Watersheds in Mountain Eco-Regions Project TA 7883-NEP, 2012, though the study is now dated. The main issue is the conversion factor for paddy rice: 1.25, based on import parity price assessment. This seems high and up-dating is required, see Table 34. The conversion factor for the value of paddy rice adopted is 1.0. The choice of this factor is also important for the value of economic benefits.

Table 34: Import Parity Price for Rice

Item Financial Economic India Long grain parboiled rice 25% broken, Bihar State FOB 1/ 325 325 Quality adjustment -0.025% -8 -8 Freight and insurance 15 15 cif Terai Cities, US$ 332 332 cif Terai Cities OER NPR 103 to US$ 34,183 cif Terai Cities SER NPR 106.09 2/ 35,209 Add Import duty at 0.1 3/ 3,418 37,601 35,209 Add Handling and storage at 0.06 2,256 2,324 Add transport cost, 50 km, NPR 10 per km/ton 500 515 40,358 38,047

Deduct costs from Farm gate: comprising: 16,870 16,924 Threshing, bagging and storage (included in gross margin) - - Transport farm to market 5/ 1,000 1,030 Milling ratio, 67% 14,070 14,070 Milling cost, NPR 1.00 per kg of paddy 4/ 1,000 1,000 Mill gate to wholesale 800 824

Import parity at farm gate 23,488 21,123

Farm gate price (threshed and bagged paddy) 21,000 21,000 Conversion 101%

1/ http://www.riceauthority.com/prices/ 2/ SERF is assumed to be 1.03 3/ http://www.customs.gov.np/upload/documents/HS%202072_20150915105900.73(2015

4/ Milling price assumed to be NPR 1.00 per kg. It is assumed no VAT is payable on rice milling 5/ Transport cost of NPR10/ton/km is assumed

118. The conversion factors used are given in Table 35.

Table 35: Economic Conversion Factors for Costs and Benefits of Flood Management Project

Item Economic Financial Exchange Rate (ER) at beginning November 2015 (US$ 1= NPR) 106.00 Shadow Exchange Rate (SER) at end January 2014 107.00 Shadow Exchange Rate Factor (SERF) 1.08 Standard Conversion Factor (SCF)1/ 0.92 Shadow Wage Rate Factor (SWRF)1/ 0.80 1.00 Civil works Investment costs 0.86 1.00 Maintenance costs 0.86 1.00 Housing Class 1 1.00 1.00

Item Economic Financial Class 2 1.00 1.00 Class 3 1.00 1.00 Class 4 1.00 1.00 Emergency relief 1.00 1.00 Rehousing 1.00 1.00 Land acquisition 1.00 Infrastructure Pukka roads 1.00 1.00 Katcha roads 1.00 1.00 Bridges adequate for motors 1.00 1.00 Power lines 1.00 1.00 Telephone lines 1.00 1.00 Paddy Cultivation 2/ Nursery costs Seed 0.91 1.00 Fertiliser & crop protection 0.91 1.00 Tools, pump set 0.91 1.00 Labour 0.80 1.00 Planting/Transplanting Tools, sprayer 0.91 1.00 Labour 0.80 1.00 Cultivation DAP 0.95 1.00 Urea 0.94 1.00 Potash 1.21 1.00 Crop protection & capital charge 0.97 1.00 Labour for cultivation 0.80 1.00 Ox cultivation 0.91 1.00 Mechanical cultivation 0.91 1.00 Labour for harvest 0.80 1.00 Materials for harvest 1.00 1.00 Land and water charges 1.00 1.00 Paddy Rice 1.00 1.00 Rice straw 0.91 1.00 Funeral costs 1.00 1.00 Medical costs 0.91 1.00 Cattle 1.00 1.00 Sheep and goats 1.00 1.00 Pigs 1.00 1.00 Poultry 1.00 1.00

1. Note: SCF and SWRF is based on 2014 review undertaken by Innovation for More Food with Less Water ADB TA 7967-REG. 2. Economic prices for Paddy cultivation from taken from Building Climate Resilience of Watersheds in Mountain Eco-Regions Project TA 7883-NEP, 2012

119. Applying these coefficients results in the CBA expressed in constant 2015 economic NPR million, see Table 36.

Table 36: Cost-Benefit Analysis – Floods with Climate Change: Constant 2015 Economic NPR million

VIII. RESULTS AND SENSITIVITY ANALYSIS OF THE CBA

Summary of the Financial and Economic Indicators

120. The Table below summarizes the key indicators in both financial and economic prices using predicted floods with climate change.

Table 37: Financial and Economic Indicators: Biring Sub-project

Financial Economic NPV -595.23 -452.81 IRR 1.3% 2.7% BCR 0.40 0.47

121. The economic value of NPV is negative, indicating that the present value of the net income from the project is less than the costs. Economic IRR is less than the assumed discount rate of 10%: IRR represents the rate of interest that drives the value of the NPV to zero, so this means the project is achieving a rate of return much less than the required rate of 10%. 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. The BCR less than unity implies the present value of future benefits is less than the present cost of implementation. In economic terms the project unviable.

122. The reason for the poor performance of the Biring project compared to the other five considered is fundamentally because of the relatively small number of houses that will be protected by flood proofing. The costs per unit area protected proposed are far too high to yield a good return on agricultural land.

123. There is a marked difference between project indicators derived from historical flood data and derived from flood data estimated with climate change. This is partly explained by the expected increase in flood area and FHR with climate change for a given return period, but mostly due to the increase in probability of occurrence of a flood of given return period: for example a flood that historically occurred once every 50 years might be expected to occur once in every 30 years in the future. The Biring project is of course economically unviable using historical flood data.

124. At pre-feasibility level it can be concluded that the project as proposed is financially and economically unviable, but this requires confirmation by a feasibility study because of the risk attached to measurement of benefits (and the probability of future floods with climate change) is significant: benefits may be greater or lesser than those estimated.

Sensitivity Analysis

125. Switching values (the change in costs and benefits required to return an NPV of zero) have been calculated in economic prices. If costs were reduced by as much as 42% of estimated levels or benefits increased by 211% an NPV of zero would result. These are very wide margin, suggesting that even allowing for measurement difficulties the proposed project would need re-considering to achieve satisfactory financial and economic indicators.

126. Sensitivity analysis was considered necessary to test the assumptions made on the resilience of houses and crops to the FHR. There is no empirical data available to match vulnerability with flood risk, so the assumptions need to be tested. The easiest way to do this is to adjust the FHR – not that the rating itself, which is based on risk, is in doubt but because an increase or decrease in the rating simulates a change in the estimate of vulnerability of infrastructure and crops. If the rating is adjusted but avoided loss changes little then it is possible that vulnerability has been wrongly estimated. Table 38 shows the impact on economic IRR for ten percent changes in the two FHR. Changes in the rating lead to small changes in the EIRR but it is clear that if vulnerability is over-estimated (i.e. houses are less vulnerable to structural damage than estimated in the analysis and crops suffer less from flood damage than predicted) then the project becomes even more unviable in economic terms. To raise EIRR to an acceptable level would require the present estimate to be a serious under- estimation of this vulnerability.

Table 38: Impact of Change in FHR on Economic IRR

Weighted Flood Hazard Area Rating % change IRR= 3% 120% 110% 100% 90% 80% 120% 4% 3% 3% 3% 3% 110% 3% 3% 3% 3% 2% 100% 3% 3% 3% 3% 2% 90% 3% 3% 3% 3% 2%

Structure Rating % Rating

Flood Hazard 80% 2% 2% 2% 2% 1%

127. A sensitivity analysis can also be carried out to investigate the effect of the proportion of the area of the flood envelope impacted by flood events of different magnitude. The data suggest for example that a relatively small proportion of the 1 in 5 year flood envelope is reported as damaged during a 1 in 5 year event; for events of greater return periods this proportion rapidly increases. See paragraphs 22 and 23. The sensitivity suggests that neither over nor under-estimation of the flood envelope will change EIRR by much.

Table 39: Impact of Change in Houses and Agricultural Area Affected on Economic IRR

Total buildings affected, % change IRR= 3% 120% 110% 100% 90% 80% 120% 4% 4% 4% 3% 3% 110% 4% 3% 3% 3% 3% 100% 3% 3% 3% 2% 2% 90% 3% 2% 2% 2% 1%

% change

Agricultural

area affected, 80% 2% 2% 2% 1% 1%

128. Data analysis also showed that not all houses in an affected area were damaged/destroyed by a flood event commensurate with the predicted size of its envelope. For frequently returning floods, only a small proportion is likely to be affected, whereas for more infrequent floods a much higher proportion suffer damage. This can be combined in a data table with the number of total buildings affected. The sensitivity analysis shows that if the proportion of houses affected by a specified flood is either over or under-estimated, then the project remains unviable in economic terms.

Table 40: Impact of Change of Houses Affected on Economic IRR

Total buildings affected, % change IRR= 3% 120% 110% 100% 90% 80% 120% 4% 4% 4% 3% 3% 110% 4% 3% 3% 3% 3% 100% 3% 3% 3% 2% 2%

change

Affected 90% 3% 2% 2% 2% 2%

buildings

damaged,% 80% 2% 2% 2% 1% 1%

129. Sensitivity analysis is also available for the impact on the amount of public infrastructure affected by floods of different return periods and the amount spent on repair of damaged houses after flood events. The results of the cost-benefit analysis are moderately sensitive to the former and insensitive to the latter.

130. The reduction in the casualty rate at Biring as a result of the project is obviously very sensitive to the mortality and morbidity rates estimated (see section IV). The rates are derived from a much larger data set and can only be indicative for the project area. The reduction in casualties is also dependent on the population living on the flood plain. While the latest census is recent, it was conducted by ward, not by geophysical area and considerable manipulation of data was required to establish the floodplain population (see section II.B). A feasibility study would establish this population more accurately.

131. The assumption made on the future growth of housing and infrastructure in the area of flood management was found to be very important (see paragraph 56). This report has already noted that population increase appears slower in Biring than in other basins. With no future growth in assets in the proposed project area the EIRR falls to -13%. This suggests that for a greater impact of the project itself, attention should be paid to other aspects of development in the project area, for example ensuring that improvements in public infrastructure are targeted to the project area to encourage settlement and development.

Poverty

132. The flood management project proposed can be assumed to be pro-poor, even from the general data gathered during a pre-feasibility study. It is likely to target beneficiaries that have marginalized themselves to relatively high-risk areas in order to gain access to resources, particularly land, that they cannot obtain elsewhere. The evidence for this is (i) the apparent substantial growth on flood plains over the last 20 years of housing and agricultural land use (see paragraph 15 and 16) and (ii) the probability that housing is of lower quality on the flood plain areas than elsewhere (see paragraphs 32 to 35).

133. If this is so, the project is not so much about protection of existing assets, which are expected to be relatively low, but about encouraging growth of assets of the beneficiary group. The cost-benefit analysis suggests that indirect benefits are an important component of total project benefits; these are benefits not directly connected with flood events but realized through market mechanisms as a result of project investment.

134. If the generation of indirect benefits is important to project success, then this implies that flood proofing is necessary to allow the development of fixed assets. While support to existing coping mechanisms is laudable and relatively cheap (for example Early Warning Systems) these tend to reduce direct costs of flooding rather than increase fixed assets of the poor. Improving coping mechanisms saves lives and movable assets but do not get people out of the vicious cycle of poverty that causes them to live on flood plains in the first place.

135. Following the argument that any project in flood-prone areas will be pro-poor, because the poorest are marginalized into living in high-risk areas, NPV and IRR only have to be demonstrated to be greater than 0 and the discount rate respectively. The Government could even consider an implicit subsidy for flood management in such areas by accepting a lower social discount rate. In the case of Biring, a social discount rate of 3% would be required.

136. A re-design of the project concept at Biring together with confirmation of the numbers of houses that will be affected, followed by a feasibility study is essential for the proposed Biring project.

Table 41: Biring Priority Basin Historical Flood Damage Data 1992-2015

SOURCED FROM MOHA/DWIDP RECORDS

District-VDC VDC basin basin 2 Place Flood Date Dead Missing Injured Affected Affec- Destroyed Dam Sheds Per- Affec- Far- Land Live- area 1 People People People People ted Houses aged destroyed sons ted ming lost stock in house- hous Eva- Routes and ha Death basin holds es cu- (m) Forest s, ha ated (Ha) JHAPA- SURUNGA 5,511 Biring Kankaii 15 September 1991 0 0 0 0 0 0 0 200 0 0 JHAPA- SURUNGA 5,511 Biring Kankaii 11 August 1997 0 0 0 0 0 0 0 0 0 0 JHAPA- RAJGADH 1,368 Biring - 25 August 1999 0 0 0 0 0 0 0 0 1 0 JHAPA- SURUNGA 5,511 Biring Kankaii 25 August 1999 0 0 0 0 7 0 0 0 0 0 JHAPA- DANGIBARI 1,724 Biring - 15 August 2000 0 0 0 0 11 0 0 0 0 0 JHAPA- GHAILADUBBA 2,168 Biring - Ward no 5 30 July 2001 0 0 0 270 0 0 0 0 10.15 0 JHAPA- SHARANAMATI 2,483 Biring Kankaii Ward no 5 16 September 2001 0 0 0 173 0 0 0 0 33.86 0 JHAPA- BUDHABARE 893 Biring - 24 July 2002 0 0 0 0 0 0 0 0 0 0 JHAPA- GHAILADUBBA 2,168 Biring - 21 August 2002 0 0 0 2700 8 0 0 0 677.2 0 JHAPA- Khudunabar KHUDUNABARI 5,108 Biring - i-8 08 July 2003 0 0 0 0 0 0 0 0 2 0 JHAPA- GHAILADUBBA 2,168 Biring - 07 July 2004 0 0 0 1000 0 0 0 0 0 0 JHAPA- GHAILADUBBA 2,168 Biring - 07 July 2004 0 0 0 0 0 0 0 0 0 0 JHAPA- GHERABARI 240 Biring - Ward no. 4 18 July 2004 1 0 0 0 0 0 0 0 0 0 JHAPA- GHAILADUBBA 2,168 Biring - Ward no-4, 23 July 2008 0 0 0 147 0 27 0 0 0 0 JHAPA- DANGIBARI 1,724 Biring - Ward No. 5 16 August 2009 0 0 0 1080 0 200 0 0 0 0 JHAPA- GHAILADUBBA 2,168 Biring - Ward No. 4 16 August 2009 0 0 0 1620 0 300 0 0 0 0 JHAPA- Ward RAJGADH 1,368 Biring - No.2,3,4 16 August 2009 0 0 0 1620 0 300 0 0 0 0 JHAPA- TAGANDUBBA 467 Biring Kankaii 16 August 2009 0 0 0 0 0 13 90 0 0 0 JHAPA- DANGIBARI 1,724 Biring - 19 August 2009 2 5 0 73 7 0 0 0 0 0

ILAM- 11,14 DANABARI 6 Biring Kankaii Ward No. 1 20 July 2010 0 0 0 21 0 4 0 0 0 0 JHAPA- GHERABARI 240 Biring - Ward No. 3 20 July 2010 0 1 0 0 0 0 0 0 0 0 ILAM- PANCHAKANY A 2,865 Biring - Ward No. 3 24 July 2010 1 0 0 5 0 0 0 0 0 0

ILAM- 11,14 DANABARI 6 Biring Kankaii Ward No. 8 23 August 2010 2 0 0 436 0 0 0 0 0 0 JHAPA- DANGIBARI 1,724 Biring - Ward No. 3 23 August 2010 0 0 0 130 0 24 0 0 23 0 JHAPA- ARJUNDHARA 2,822 Biring - Ward No. 3 26 August 2010 2 0 0 4267 101 106 0 0 67 36 JHAPA- Khudunabar KHUDUNABARI 5,108 Biring - i-8 10 July 2011 0 1 0 0 0 0 0 0 0 JHAPA- Arjundhara- ARJUNDHARA 2,822 Biring - 5 19 July 2011 0 0 0 0 1 0 0 0 0 JHAPA- Khudunabar KHUDUNABARI 5,108 Biring - i-3 26 July 2011 1 0 0 0 0 0 0 0 0 JHAPA- Arjundhara- ARJUNDHARA 2,822 Biring - 5 28 July 2011 1 0 0 0 0 0 0 0 0 JHAPA- Budhabarae BUDHABARE 893 Biring - -3 21 August 2011 1 0 0 0 0 0 0 0 0

ILAM- 11,14 DANABARI 6 Biring Kankaii Danabari-9 10 September 2011 1 0 0 0 0 0 0 0 0 JHAPA- GHERABARI 240 Biring - Ghorabari-5 16 July 2012 0 0 0 0 0 0 0 0 0

Volume 4: Appendix E Lahmeyer International in association with Total Management Services