Document Produced under Grant - Asian Development Bank · Document Produced under Grant This document does not necessarily reflect the views of ADB or the Government concerned, and

Document Produced under Grant

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.

Project Number: 45206 May 2016

Grant 0299-NEP: Water Resources Project Preparatory Facility

Final Report – Volume 2 (1 of 4)

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

GOVERNMENT OF NEPAL Ministry of Irrigation

Department of Water Induced Disaster Prevention

Technical Assistance Consultant’s Report

Grant No. 0299-NEP: Water Resources Project Preparatory Facility May 2016

Package 3: Flood Hazard Mapping and Preliminary Preparation of Flood Risk Management Projects

Prepared by Lahmeyer International

in association with Total Management Services

Final Report – VOLUME 2

GOVERNMENT OF NEPAL

Ministry of Irrigation

Department of Water Induced Disaster Prevention

FINAL REPORT

VOLUME 2

APPENDIX A – RAINFALL ANALYSIS AND CLIMATE CHANGE TRENDS

APPENDIX B – HYDROLOGICAL ANALYSIS AND MODELLING

Water Resources Project Preparatory Facility

Package 3: Flood Hazard Mapping and Preliminary Preparation of Flood Risk Management Projects

Grant No. 0299-NEP

MAY 2016

This consultant’s report does not necessarily reflect the views of the ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. All the views

expressed herein may not be incorporated into the proposed project’s design.

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

Volume 2: Appendix A Lahmeyer International in association with Total Management Services

CONTENTS

VOLUME 1

MAIN REPORT

VOLUME 2

APPENDIX A – RAINFALL ANALYSIS AND CLIMATE CHANGE TRENDS

APPENDIX B – HYDROLOGICAL ANALYSIS AND MODELLING

VOLUME 3

APPENDIX C – HYDRAULIC MODELLING, HAZARD AND RISK MAPPING

VOLUME 4

APPENDIX D – BASIN SCREENING AND RANKING

APPENDIX E – CONCEPT NOTES FOR BIRING BASIN APPENDIX F – CONCEPT NOTES FOR MAWA RATUWA BASIN

VOLUME 5

APPENDIX G – CONCEPT NOTES FOR LAKHANDEHI BASIN APPENDIX H – CONCEPT NOTES FOR EAST RAPTI BASIN APPENDIX I – CONCEPT NOTES FOR WEST RAPTI BASIN APPENDIX J – CONCEPT NOTES FOR MOHANA BASIN

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


APPENDIX A

RAINFALL ANALYSIS AND CLIMATE CHANGE TRENDS

CONTENTS

I. RAINFALL EVALUATION ..................................................................................................... 1

Physiographic controls upon the rainfall distribution .................................................... 1 Raingauge Network Density ......................................................................................... 4 Sample interstation correlations (24 hours) for the Terai ............................................. 4

II. CLIMATE CHANGE EFFECTS UPON THE RAINFALL ...................................................... 7

GCM Projections .......................................................................................................... 7 AR4 Output from DHM Climate Portal ................................................................ 7 AR5 GCMs from World Bank Climate Change Knowledge Portal ..................... 9 CMIP5 (AR5) ....................................................................................................... 9

The Validity of GCM Results with respect to Flood Calculations ............................... 16 Evidence Based Instrumental Trend Analysis ............................................................ 17

Single Station Rainfall Trends .......................................................................... 17 Inter-Study Comparison .............................................................................................. 26 24-Hour Maximum Rainfall ......................................................................................... 27

III. RAINFALL INPUT FOR BASIN-RUNOFF MODELLING .................................................... 29

IV. CONCLUSIONS ................................................................................................................... 31

V. REFERENCES ..................................................................................................................... 32

ANNEX 1: MONTHLY RAINFALL SUMMARIES ......................................................................... 33

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


LIST OF FIGURES

Figure 1: Location of rainfall stations for rainfall-altitude analysis ................................................... 1 Figure 2: Face-value mean rainfall-altitude relationship .................................................................. 3 Figure 3: Semi-Variogram of Stations in the Terai and Siwaliks ...................................................... 6 Figure 4: Various climate change scenarios .................................................................................... 8 Figure 5: Stations representing grid in Terai .................................................................................. 11 Figure 6: Historic and projected rainfall (RCP2.6) for July ............................................................. 11 Figure 7: Historic and Projected rainfall (RCP4.5) for July ............................................................ 12 Figure 8: Historic and Projected rainfall (RCP4.5) for July ............................................................ 12 Figure 9: Grids representing Himalayan area ................................................................................ 13 Figure 10: Percentage Increase in Rainfall (RCP4.5) .................................................................... 14 Figure 11: Face-value means of annual and monsoonal variation + second order trend

(station 1216 Siraha) ...................................................................................................... 17 Figure 12: Face-value means of annual and monsoonal variation + second order trend

(station 1408 Damak) ..................................................................................................... 18 Figure 13: Face-value means of annual and monsoonal variation + second order trend

(station 1110 Tulsi) ......................................................................................................... 19 Figure 14: Face-value means of annual and monsoonal variation + second order trend

(station 911 Parwanipur)................................................................................................. 20 Figure 15: Annual 24-hour maxima (station 1408 Damak) ............................................................ 21 Figure 16: Annual 24-hour maxima (station 1216 Siraha) ............................................................. 22 Figure 17: Annual 24-hour maxima (station 1110 Tulsi) ................................................................ 22 Figure 18: Annual 24-hour maxima (station 911 Parwanipur) ....................................................... 23 Figure 19: Location of stations for trend analysis .......................................................................... 23 Figure 20: 24-hour maxima Rainfall Trends: 17 stations in the Terai and Siwaliks ....................... 25 Figure 21: 24-hr Rainfall Frequencies: Station 405, Chisapani Karnali ......................................... 27 Figure 22: 24-hr Rainfall Frequencies: Station 911, Parwanipur ................................................... 28 Figure 23: 24-hr Rainfall Frequencies: Station 1216, Siraha ......................................................... 28

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


LIST OF TABLES

Table 1: List of stations for rainfall-altitude analysis ........................................................................ 2 Table 2: Network density for Nepal .................................................................................................. 4 Table 3: List of stations for interstation correlation analysis ............................................................ 5 Table 4: Interstation correlations ...................................................................................................... 5 Table 5: Interstation distances (km) ................................................................................................. 5 Table 6: Annual Rainfall averaged over Nepal region from observations and models .................... 8 Table 7: List of stations for comparison ......................................................................................... 10 Table 8: CMIP5: Sixteen GCM comparisons for east to west stations along the Terai

(numbered on map) ........................................................................................................ 10 Table 9: Increases in Rainfall based upon CMIP5 – 16 model ensemble medians,

RCP4.5 for July ............................................................................................................... 13 Table 10: 16-Model-based Rainfall increases (%) from Climate Change, RCP4.5 ....................... 15 Table 11: Ranges of extreme modelled rainfall ............................................................................. 16 Table 12: Summary of monsoon rainfall trends from long-period, low-fragmented

station records ................................................................................................................ 20 Table 13: Summary of monsoon rainfall trends from 24-hour maxima, low-fragmented

station records ................................................................................................................ 21 Table 14: List of stations for trend analysis .................................................................................... 24 Table 15: Result of trend analysis .................................................................................................. 24 Table 16: Terai Rainfall Projections (monsoon season) based upon instrumental

trends .............................................................................................................................. 25 Table 17: Comparison of data in two studies ................................................................................. 26 Table 18: Adopted mean rainfall-altitude relationships, normalised with respect to the

rainfall at 1000 metres. ................................................................................................... 29 Table 19: Monthly rainfall summary for Damak, Station Nr. 1408 ................................................. 34 Table 20: Monthly rainfall summary for Parwanipur, Station Nr. 911 ............................................ 35 Table 21: Monthly rainfall summary for Tulsi, Station Nr. 1110 ..................................................... 36 Table 22: Monthly rainfall summary for Siraha, Station Nr. 1216 .................................................. 37

WRPPF-Package 3: Flood Hazard Mapping & Preliminary Preparation of Risk Management Projects 1 Final Report May 2016


I. RAINFALL EVALUATION

1. In this section the problems and issues of defining rainfall input are addressed, both

on the assumption of stationarity of the dataset, and incorporating climate change trends.

Physiographic controls upon the rainfall distribution

2. Even taking the available data at face value (‘business as usual assumption’) it is necessary to take cognizance of the ‘station typicality’, given the strong physiographic influences that apply to all mountainous terrains. The most obvious co-variable is that of

altitude. Therefore the face-value rainfall-altitude relationship, is developed using data from

88 stations, whose location is shown in Figure 1 below.

Figure 1: Location of rainfall stations for rainfall-altitude analysis



Table 1: List of stations for rainfall-altitude analysis

St. no. Name District St. no. Name District

101 Kakerpakha Baitadi 1022 Godavari Lalitpur

103 Patan (West) Baitadi 1023 Dolal Ghat Kabhre

201 Pipalkot Bajhang 1024 Dhulikhel Kabhre

202 Chainpur (West) Bajhang 1030 Kathmandu Airport Kathmandu

203 Silgadhi Doti Doti 1102 Charikot Dolkha

205 Katai Doti 1103 Jiri Dolkha

206 Asara Ghat Achham 1104 Melung Dolkha

208 Sandepani Kailali 1107 Sindhuli Gadhi Sindhuli

209 Dhangadhi Kaliali 1108 Bahun Tilpung Sindhuli

210 Bangga Camp Achham 1109 Pattharkot(East) Sarlahi

301 Mugu Mugu 1110 Tulsi Dhanusa

302 Thirpu Kalikot 1112 Chisapani Bazar Dhanusa

303 Jumla Jumla 1115 Nepalthok Sindhuli

309 Bijayapur (Raskot) Kalikot 1116 Hariharpugadhi Sindhuli

403 Jamu Surkhet 1201 Namche Solukhumbu

404 Jajarkot Jajarkot 1202 Chaurikhark Solukhumbu

405 Chisapani (Karnali) Bardiya 1204 Aisealukhark Khotang

406 Birendra Nagar Surkhet 1207 Mane Bhanjyang Okaldhunga

408 Gulariya Bardiya 1208 Dwarpa Khotang

501 Rukumkot Rukum 1210 Kurule Ghat Khotang

502 Shera Gaun Rukum 1211 Khotang Bazar Khotang

504 Libang Gaun Rolpa 1213 Udayapur Gadhi Udayapur

505 Bijuwar Tar Pyuthan 1215 Lahan Siraha

509 Ghorahi Dang 1216 Siraha Siraha

609 Beni Bazar Myagdi 1224 Sirwa Solukhumbu

610 Ghami Mustang 1301 Num Sankhuwasabha

701 Ridi Bazar Gulmi 1302 Dumuhan Sankhuwasabha

702 Tansen Palpa 1305 Leguwa Ghat Dhankuta

704 Beluwa Nawalparasi 1306 Munga Dhankuta

722 Musikot Gulmi 1307 Dhankuta Dhankuta

801 Jagat (Setibas) Gorkha 1308 Mul Ghat Dhankuta

807 Kunchha Lamjung 1309 Tribeni Dhankuta

808 Bandipur Tanahun 1311 Dharan Bazar Sunsari

810 Chapkot Syangja 1316 Chatara Sunsari

903 Jhawani Chitawan 1317 Chepuwa Sankhuwasabha

904 Chisapani Gadhi Makwanpur 1319 Biratnagar Morang

907 Amlekhganj Bara 1322 Machuwaghat Dhankuta

910 Nijgadh Bara 1402 Pangthung Panchthar

912 Ramoli Bairiya Routahat 1403 Lungthung Taplejung

1002 Arughat Dhading 1406 Memeng Jagat Panchthar

1004 Nuwakot Nuwakot 1407 Ilam Tea Estate Ilam

1005 Dhading Dhading 1408 Damak Jhapa

1008 Nawalpur Sindhupalchok 1409 Anarmani Birta Jhapa

1013 Sundarijal Kathmandu 1418 Angbung Panchthar



3. The face value of mean rainfall-altitude relationship is shown in Figure 2 below.

Figure 2: Face-value mean rainfall-altitude relationship

Note: An instrumental error of ±7% is assumed, and shown as the vertical error bounds. Experience worldwide indicates that splash, micro-aerodynamic turbulence and wetting errors are likely to result in a mean under-catch of about 7%, or more in the case of windy raingauge stations

4. Although no clear rainfall-altitude is apparent, the following broad conclusions may be

drawn:

(i) Raingauge stations in the Terai, of less than 500 metres in altitude, can expect anywhere between 1000 and 2500 mm of rain per year, which does not greatly differ from the rainfall in the Siwalik foothills, up to an elevation of ~2000 metres.

(ii) At about 3000 metres the precipitation decreases to about 1000 mm, and may be expected to decrease further to <500 mm at 5000 metres.

(iii) As expected, there is a greater spread of data in the more mountainous region, particularly at 1500±500 metres. Hence the typicality of any raingauge within this altitude range is particularly prone to misrepresentation.

(iv) Forced lifting (and hence cooling) is obviously important but the rainfall-altitude relationship, by itself, is entirely inadequate to describe the rainfall distribution. Rather, the rainfall can be expected to be a non-linear multi-variate function of altitude, altitude2, trend, aspect, barrier, rain-shadow and ‘valley air-flow’, which has yet to be analysed and described mathematically.

(v) In broad terms there is a ‘trend effect’ of increasing rainfall eastwards along the Terai, parallel to the mountain front. This effect is most noticeable in the peak monsoon period (July), when the trend averages +28 mm per hundred kilometres. Incidentally this same trend continues far beyond the eastern Nepalese border, and into Bhutan.

0

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Rainfall-Altitude in Nepal (88 stations)

The broken line is the form of the

expected 'theoretical' rainfall-

altitude distribution (uncalibrated)



This trend effect is at least as strong as, if not much stronger than, the altitude effect, but may be masked on the meso-scale by local physiographic effects. These meso-scale rainfall variations cannot be satisfactorily represented with the existing raingauge network density.

5. Other physiographic effects which are very strong include ‘aspect’, ‘rain-shadow’ and ‘barrier effects’ of successive ranges of hills of similar mean altitude. For example, Singh et al

(2001) emphasised the strong rain-shadow and barrier effects in reporting on the middle

Himalayas, where the rainfall gradient is 106 mm /100m on windward slopes, as opposed to

13 mm / 100m on leeward slopes. This severely impacts upon the choice of design rainfall

amounts, as discussed below under ‘rainfall input for basin-runoff modelling’.

Raingauge Network Density

6. As in almost all countries, the logistics, and in particular the cost of maintaining a

network, results in the national network being very much sub-optimal for water resources and

flood estimation work. The network density for Nepal is given in Table 2 below:

Table 2: Network density for Nepal

Terrain Total area, km2 Network total, Φ Network density, §

All Nepal 147,000 264 550

Terai 25,000 67 370

Siwaliks and low Himalayas 40,000 92 430

Optimum raingauge density ʯ - About 20 to 45

Φ Long period raingauges in the National Hydrometric Network.

§ Average area, Km2 represented by each gauge.

ʯ The optimum raingauge density in mountainous environments is not amenable to precise characterisation.

This estimate is based upon several research reports, such as that of Lopez et al., (2015), Volkman et al.

(2010).

7. The ability to infer spatially distributed data from point measurements is strongly

dependent on the number, location and reliability of measurement stations. In general the

WMO recommendations for the ideal minimum raingauge density in tropical mountain areas

is one station per 100 to 250 km2, and the acceptable density of one station per 250 to 1000

km2. We regard these as too generous for the complex physiographic controls which prevail

in the project area. In particular, we note that a reduced rain gauge network density in the

higher parts of the catchment results in a noticeable decline in performance indices.

8. The Bureau of Indian Standards recommends a minimum raingauge density, in hilly

areas with heavy rain, of 130 km2 per gauge.

Sample interstation correlations (24 hours) for the Terai

9. For accurate rainfall estimation, the clearest indication of required network density is

inferred from the interstation correlations. Hence, the Pearson moment correlations for non-

zero rain-days is given for 9 raingauge stations with long periods of record, mainly in the Terai.



Table 3: List of stations for interstation correlation analysis

Station Name District

501 Rukumkot Rukum

706 Dumkauli Nawalparasi

708 Parasi Nawalparasi

716 Taulihawa Kapilbastu

906 Hetauda Makwanpur

910 Nijgadh Bara

911 Parwanipur Bara

912 Ramoli Bairiya Rautahat

1107 Sindhuli Gadhi Sindhuli

10. Correlations with higher relief stations are shaded.

Table 4: Interstation correlations

Station 1107 912 911 910 906 716 708 706

501 0.17 0.13 0.16 0.18 0.14 0.13 0.2 0.13

706 0.22 0.14 0.13 0.15 0.50 0.13 0.13

708 0.23 0.36 0.37 0.28 0.11 0.44

716 0.17 0.33 0.31 0.24 0.16

906 0.24 0.15 0.14 0.20

910 0.31 0.44 0.40

911 0.21 0.54

912 0.26

Table 5: Interstation distances (km)

Station 1107 912 911 910 906 716 708 706

501 363 328 291 291 276 126 157 192

706 233 137 92 107 85 120 65

708 176 185 143 159 142 58

716 291 241 198 216 200

906 92 57 33 28

910 79 30 27

911 103 43

912 65

11. It will be noted that the highest correlation, 0.54, corresponds to an interstation

distance of 43 km. This is consistent with a general ‘rule of thumb’ that, in high relief areas, correlations of 0.50 and above, cannot be expected at inter-station distances of more than

about 40 km.



12. The generally low correlations between adjacent stations indicates the limitations

involved in using inter-station correlation to synthesize gaps in individual station time-series.

The estimated semi-variogram of Figure 3, based mainly upon stations in the Terai with a few

stations in the Siwaliks, suggests that reliable inter-station comparison, for purposes of data

continuity, occurs at distances of less than about 30 km. Such inter-station distances prevail

in region 7, but not elsewhere.

Figure 3: Semi-Variogram of Stations in the Terai and Siwaliks

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Raingauge Network Semi-Variogram for stations in Table 3



II. CLIMATE CHANGE EFFECTS UPON THE RAINFALL

13. In principle, the indisputable overall global warming will have the effect of increasing

the precipitable moisture, and hence the rain over the lower Himalayas of Nepal. On the other

hand the redistribution of South Asian circulation could have a reverse effect by either

weakening or redistribution the monsoonal airflow. Despite decades of gradually improving

AOGCMs, the relative dominance of these conflicting processes is still unresolved. Forward

projections of rainfall variation due to climate change are always difficult to assess, and doubly

so in the case of the Indian Monsoonal region. Currently the quantitative impact of global

warming upon the Himalayan monsoonal rainfall is one of the great unanswered questions of

climate change science, in which no definitive quantitative assessments are yet possible.

Therefore, there is a range of semi-quantitative indications in which a provisional consensus

is the best that can be accessed for technical planning purposes.

14. It is most unfortunate that global climatic models (GCMs), or their downscaled

derivatives have come to be relied upon as the sole predictor of climate-change induced

rainfall trends. This is like ‘putting all one’s eggs in a basket –with holes’. Since all the methodologies have their limitations with respect to the requisite reliability it is prudent to

assess the rainfall trends from as many perspectives as possible.

15. There are at least four possible approaches:

(i) GCM projections

(ii) Evidence based instrumental trend analysis

(iii) Precipitable moisture profiling, and

(iv) Depth-duration envelope drift.

16. Unfortunately the relevant data were not available to undertake methods 3 and 4, and

consequently this study is limited to only two approaches, with all the implicit uncertainties that

these entail.

GCM Projections

17. In order to assess the internal consistency of the modelling approach, two sets of

GCMs were analysed separately, effectively as two independent studies. The first of these

was the climate portal posted by the Nepal Department of Hydrology and Meteorology (DHM),

and the second was another climate change portal for southern Asia, hosted by the World

Bank.

AR4 Output from DHM Climate Portal

18. The DHM climate portal uses the following regional climatic models (RCMs): PRECIS,

based upon the atmospheric component of HadCM3; RegCM4, and WRF4. These model

simulations are based upon the IPCC 4th assessment (AR4), and are now superseded by the

5th assessment (AR5). They are here regarded as less reliable than the AR5 experimental

data. Nevertheless, the data of Table 6 yields a ‘first pass’ in which the mean ratio of forward

projected rainfall to historic rainfall was 1.034. The baseline data employed was 1971 to 2000,

and the simulation period was 2030 to 2060.



19. It is noted that this 2% increase in overall rainfall is at least an order of magnitude less

than the cumulative uncertainties of the modelling. Moreover, this coarse assessment is based

upon the mean of a range of grid squares and model results, rather than the more appropriate

median values. The latter are not given, but had they been used it is quite possible that no net

increase in rainfall due to climate change would have resulted.

Table 6: Annual Rainfall averaged over Nepal region from observations and models

Observed

RCM HadCM3 RCM ECHAM5 RCM RegCM RCM WRF

Raw BC (ϕ) Raw BC Raw BC Raw BC

Mean 138 112 152 129 139 3 140 79 140

Std Dev 152 102 152 110 153 1.5 153 72 151

Corr Coef - 0.88 0.93 0.86 0.92 0.63 0.93 0.67 0.92

Max Rain 572 355 579 406 652 7 649 324 575

Min Rain 1 1 3 4 3 0 3 1 4

Source: http://www.dhm.gov.np/portal_data/Nepal%20Climate%20Data%20Portal-

User%20Guide%20v6.pdf (Nepal Climate Data Portal, User Manual)

‘(ϕ)’ = Bias Corrected.

20. Note, all four models used the A1B scenario. The current emissions trend lies

somewhere between A1B and B1. There is no direct equality between the RCP and A/B

families of scenarios, but their correspondence may be judged from Figure 4.

Figure 4: Various climate change scenarios

Source: Wikipedia https://en.wikipedia.org/wiki/Representative_Concentration_Pathways

21. GCMs and their downscaled derivatives yield substantial biases in total amount,

frequency and intensity of rainfall. To some extent this can be compensated by ‘bias correction’. In the case of DHM’s modelling the data are presented both as raw and as ‘bias corrected’. Both are given in adjacent columns in Table 6.

22. The bias correction process is complex, dealing with statistical problems arising from

grid averaging and disaggregation. It involves the truncation of spurious low rainfall values,

https://en.wikipedia.org/wiki/Representative_Concentration_Pathways



and applies an empirical distribution of historic rainfall to the modelled rainfall intensities.

Although the bias corrected data are more consistent with the historic data, the correction

process has the effect of introducing a new level of uncertainty, comparable in magnitude to

the RCM spread of the climate projections, and hence must be treated with caution.

23. Analysis of the HADGEM2 (Hadley Centre Model of UK Met office) climate model, and

their application in terms of estimated 24-hour maximum rainfall, for the RCP4.5 scenario,

yields the following mean rates of increasing rainfall (assuming linear growth):

West Rapti +2.0 mm per decade Mawa Ratuwa +2.3 mm per decade Jhim +6.8 mm per decade

24. These are very modest rainfall growth rates compared to basins with a significant

fraction in the ‘Himalayan’ zone, discussed below.

AR5 GCMs from World Bank Climate Change Knowledge Portal

25. The usual GCM approach is to assume a climatic evolutionary scenario, and to

undertake modelling using a wide range of models and implicit assumptions.

26. Three scenarios were used in this study, RCPs 2.6, 4.5 and 8.5, in which the numbers

refer to the radiative forcing in +W.m-2. Of these the 8.5 is probably unduly pessimistic. The

2.6 is now probably unachievable (due to slow international response on emissions), which

leaves the 4.5 scenario as probably close to the realistic likely emissions trend. The RCP

trends are summarised in Figure 4.

27. There is a disconcerting array of over 20 GCMs which yield widely disparate forward

modelled projections. Realistic error bounds have not been quoted for these models.

CMIP5 (AR5)

28. As a check upon the DHM’s results, the computer modelling over a different set of

models utilized the Coupled Model Inter-comparison Project Phase 5, usually known as

‘CMIP5’, was used to assess the overall most likely rainfall trends over the following averaged time steps: historical data up to 2009, 2020 to 2039, 2040 to 2059 and 2060 to 2079. This was

repeated for scenarios RCP2.6, RCP 4.5 and RCP 8.5.

29. CMIP5 uses up to 20 models, used by dozens of modelling groups worldwide. Of these

models, the 16 used in this analysis were as specified under the World Bank Climate Change

Knowledge Portal, details of which may be accessed at:

http://sdwebx.worldbank.org/climateportal/index.cfm?page=resource#user_guide. Or

http://sdwebx.worldbank.org/climateportal/documents/WB_Climate_Change_Knowledge_Portal

_UsersGuide.pdf

30. All of these models are based upon a grid zone of about 90 x 90 km. In order to assess

the likely geographic variation along the Terai, eight raingauge stations were selected from

WNW to ESE as being representative of the eight grid zones. These nominal stations are

numbered 105, 209, 409, 510, 711, 911, 1216, and 1313. It is important to note that, for the

purposes of modelling they are not point rainfall estimates, but averaged grid square rainfalls.

http://sdwebx.worldbank.org/climateportal/index.cfm?page=resource#user_guide

http://sdwebx.worldbank.org/climateportal/documents/WB_Climate_Change_Knowledge_Portal_UsersGuide.pdf

http://sdwebx.worldbank.org/climateportal/documents/WB_Climate_Change_Knowledge_Portal_UsersGuide.pdf



Table 7: List of stations for comparison

Station No. Name Serial number used for graph

105 Mahendranagar 1

209 Dhangadhi 2

409 Khajura 3

510 Koilabas 4

711 Dumkibas 5

911 Parwanipur 6

1216 Siraha 7

1313 Biratnagar 8

31. A summary of these model results is tabulated below:

Table 8: CMIP5: Sixteen GCM comparisons for east to west stations along the Terai (numbered on map)

CMIP5:Mean July historic and projected rainfall CCKP Median of 16

models Scenario RCP2.6

time period \ station 105 209 409 510 711 911 1216 1313

2060 to 2079 300 245 260 325 340 395 400 270

2040 to 2059 305 350 263 320 316 330 400 285

2020 to 2039 320 391 367 395 310 382 370 316

1900 to 2009 357 419 419 442 569 500 421 533




2060 to 2079 230 250 250 350 290 340 350 370

2040 to 2059 240 300 290 310 280 340 360 251

2020 to 2039 270 303 300 286 280 310 370 300

1900 to 2009 357 419 419 442 569 500 421 533




2060 to 2079 210 260 305 345 300 335 400 350

2040 to 2059 220 380 330 320 306 380 380 310

2020 to 2039 240 355 270 300 295 300 330 320

1900 to 2009 357 419 419 442 569 500 421 533

CCKP -World Bank - Climate Research Unit. University of E. Anglia-CCRC

32. Figure 5 shows the stations approximating to the grid squares used in Table 8.



Figure 5: Stations representing grid in Terai

33. The above tables are also presented graphically as Figure 6 to Figure 8 below.

Figure 6: Historic and projected rainfall (RCP2.6) for July

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Historic and Projected July rainfall: medians of 16 models, RCP 2.6

2060 to 2079 2040 to 2059 2020 to 2039 1900 to 2009



Figure 7: Historic and Projected rainfall (RCP4.5) for July

Figure 8: Historic and Projected rainfall (RCP4.5) for July

34. Note that in the above figures the west-east axis refers to progressive grid squares

along the Terai whilst the point numbers 1 to 8 refers to the grid squares approximated by the

stations indicated in Figure 5. The vertical axis refers to the time-averaged June rainfall,

determined as the interpolated median of 16 GCMs. That is, each point refers to the projected

median rainfall from a very wide range of model conceptualizations.

35. The above model results are based upon those grid squares which lie predominantly

within the Terai and Siwaliks. However, a detailed comparison between grid squares

representing the ‘lowland and piedmont’ squares, and the more northerly ‘Himalayan’ squares, reveals a dramatic contrast. The former yield little or no increase in rainfall over time (and

indeed, sometimes a decrease in rainfall), whereas the latter grid squares yield changes

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2060 to 2079 2040 to 2059 2020 to 2039 1900 to 2009

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2060 to 2079 2040 to 2059 2020 to 2039 1900 to 2009



varying between -23% and +128%. The average 39% increase (2020 to 2080, RCP4.5) is

roughly consistent with previous modelling, anecdotal observations and popular perceptions.

36. These ‘Himalayan’ grid-square analyses are summarised in Table 9 below. This table

is not based on any particular stations. It is based upon the grid squares used by the model.

Station labels are used as an indication of where the grid squares are located. The station

number is roughly at the center of the grid square. The grid location is shown in Figure 9

below.

Figure 9: Grids representing Himalayan area

Table 9: Increases in Rainfall based upon CMIP5 – 16 model ensemble medians, RCP4.5 for July

Station 202 304 308 502-603 803-814 1406

Grid 1 2 3 4 5 6

2020-39 -0.6 111.1 24.3 78.8 -22.9 20.0

2040-59 3.8 91.7 33.8 62.6 -30.1 33.1

2060-79 17.0 128.1 51.0 93.2 -12.9 21.8

37. Notes to Table 9:

(i) The data are generated within each model grid square. The station number is nominal, to give a sense of the approximate area involved. Grid squares 1 to 6 in Figure 9 are the same as ‘stations 202 to 1406 in Table 9.

(ii) Grid square/station 102 yields an anomalously low increase in rainfall for which there is no obvious explanation.

(iii) Grid square/station 803-814 also yields an anomalously low increase in rainfall for



which there are three evident reasons. Firstly, this grid square has a much lower mean elevation due to the inclusion of almost the entire Kathmandu Valley. Secondly, the grid square is the most southerly of the various ‘Himalayan’ grid squares, incorporating a significant fraction in the Terai. Thirdly, this grid square has a much higher historic rainfall baseline than the other ‘Himalayan’ grid squares, resulting in a negative bias in the percentage increase.

(iv) Of the 16 model results only the median values were considered. Higher increases in rainfall would be indicated if the means were used. Nevertheless, the median values are considered in order to minimize the spurious effects of extreme outlier estimates.

(v) Table 9 and Figure 10 consider only the RCP4.5 scenario, which is currently regarded as the most realistic. For comparison, the calculations were also carried out with the same models using the RCP2.6 scenario. The latter yielded an average of 29% smaller change in rainfall. The two data sets yielded a correlation coefficient of 0.84.

38. For ease of visualization, these results are also plotted nomographically in Figure 10.

Figure 10: Percentage Increase in Rainfall (RCP4.5)

39. The wide spread of modelled changes in rainfall raise a problem regarding practical

application for flood model inputs. It is reasonable to discard the highest and lowest climate-

changes in rainfall on the grounds that the grid squares used to generate these extremes are

contain spatial components which are either too far north or too far south to be truly

representative of those basins which drain the central Himalayan belt. Of the remaining data

we pragmatically suggest using the upper and lower bounds based upon the 35- and 65 -

percentiles of the data. That is:

-40,0

-20,0

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0 1 2 3 4 5 6

Pe

rce

nt

cha

ng

e r

ela

tive

to

19

00

-20

09

ba

seli

ne

Grid squares, west to East

Percentage Increase in Rainfall

based upon CMIP5 - 16 model ensemble medians, RCP4.5

2020-39 2040-59 2060-79



Table 10: 16-Model-based Rainfall increases (%) from Climate Change, RCP4.5

Time frame

Terai-Siwalik Zone Himalayan Zone

Lower bound: 35 percentile

Upper bound: 65 percentile

2020-2039 0 15 38

2040-2059 0 26 41

2060-2079 0 21 62

40. Note that these projected increases in rainfall are percentages relative to a nominal

model baseline of 1900 to 2009. In practice the effective years of rainfall data are as in Annex

1.

41. The ‘no-climate-change’ in the Terai is based upon both modelling and historic trends.

Although we take this average as zero there will be local positive and negative anomalies. The

historic annual and monsoonal data have a mean of 0.0% but a standard deviation of 8.6%.

42. These factors are ‘averages of averages’, based upon only partially calibrated process

studies upon which the 16 models are conceptualized. They will certainly be modified as time-

series of data are extended into the future. For the present, however, these factors are the

best that we can suggest, and constitute at least a working hypothesis.

43. The following generalized conclusions from the CMIP5 group of models are drawn:

(vi) Regardless of the selected scenario, all the median rainfall projections for the Terai, without exception, result in a substantial decrease in June (peak monsoonal) rainfall.

(vii) The modelling preserves the existing geographic trend of increasing rainfall with easterly distance along the Terai. For the month of June, and for scenarios 2.6, 4.5 and 8.5, these trends are 12, 16 and 24 mm per 100 km, respectively (compare the historic 18 mm.100 km-1 for the whole monsoon period).

(viii) The modelling also draws a major contrast between rainfall in the Terai and Siwaliks as compared with the Himalayan zone to the north. Rainfall in the latter is set to increase over a wide range of values, roughly corresponding with the position of the modelled grid square relative to the physiography.

(ix) The Kathmandu valley, or more specifically, the grid square which encompasses the Kathmandu Valley, appears to be an exception to the trend of increasing rainfall within the Mid-Nepalese Himalayan zone. This requires verification by evidence-based instrumental data (beyond the scope of this project).

(x) There is great consistency between rainfalls in the various forward projected time intervals (20 years per interval) as compared with a huge inconsistency with the backward validation for the historic record. In other words the models imply huge change in the rainfall regime over the next 20 years, but very little change thereafter. This is the reverse of expectations. Hence this outcome is suspicious, and throws some doubt upon the model’s ability to capture the relevant processes and calibration.

(xi) Notwithstanding the modelling limitations and large implicit error bounds, the median RCP4.5 data facilitate provisional factors for projecting rainfall increases in flood model inputs.



The Validity of GCM Results with respect to Flood Calculations

44. These results underscore the basic difficulties of applying GCMs to rainfall trend

prediction. We note the following very important caveats to the modelling results:

45. Caveat 1: None of the AOGCMs or their derivatives are designed for hydrologic

applications, even though that is how they are now often used (misused?). It is therefore

essential to note that such models are highly successful in replicating regional changes in

temperature, but have a much less successful track-record in rainfall. That is, the model

accuracy may be, for example, better than ±0.5°C, whilst outputting worse than ±20% in

rainfall estimation, for the same model simulation.

46. Caveat 2: The above tables and histograms are based upon the CMIP5 median of 16

model results. When considered uncritically this condensation of data tends to under-

emphasize the huge overall spread of data between the different model outputs. In fact the

average spread of data for the 16 models may be expressed as percentages of the median

value, as follows:

Table 11: Ranges of extreme modelled rainfall

RCP 2.6 RCP 4.5 RCP 8.5

Average of highest rainfall estimates (as % of median) 242 271 266

Average of lowest rainfall estimates (as % of median) 13 8 7

Average of 8 grid squares, and 3 forward projected time periods, Medians by visual interpolation

47. Thus, although the median modelled rainfall projections are all less than the historic

data, approximately 20% of the modelled results exceed the historic data. In this study it is

assumed that the most extreme modelled rainfall projections are not credible.

48. Caveat 3: The model input implicitly incorporates averaging errors which are

compounded within the coarse grid conceptualization. The models fail to capture processes

which operate according to the fine-scale physiography. Downscaling of the models does little

to correct these errors, and may even be misleading in creating an illusion of high accuracy.

That is, greater precision ≠ greater accuracy!

49. Caveat 4: Reduced/increased monthly or seasonal rainfall does not necessarily

correspond with reduced/increased 24-hour rainfall although, as it happens, neither the

seasonal or daily historic rainfall yield any discernible trend which could be attributable to

climate-change.

50. Caveat 5: A test of model veracity is the continuity between historic and current

instrumental trends, and projected model forecasts. Given the flat-zero historic rainfall trend,

any model projections which indicate a major early (2015-2035) change followed by

subsequent lesser changes, are suspect.

51. The ‘bottom line’ of the various climatic modelling results, with respect to rainfall, is

that the older DHM modelling, based upon the mean of six model outputs, suggests a 6% to

20% increase in annual rainfall by 2045. On the other hand, a more up-to-date group of models

based upon the WB climate-change portal outputs for 2020 to 2080, and upon the median of

16 model outputs, indicates no discernible change for the Terai-Siwalik region of Nepal, but a



very substantial increase in rainfall for the mid-altitude Himalayan zone immediately

northwards.

Evidence Based Instrumental Trend Analysis

52. Single isolated days of missing data were ‘synthesized’ by comparison or correlation with the two nearest stations wherever the adjacent station-days were <20 mm. Wherever a

missing daily total was bracketed by high rainfall days (>20mm), or occurred during a likely

high rainfall day within the monsoon period, the daily total, and hence monthly total was

regarded as intractable, and hence not included in the time-series analysis. Months with more

than four days of non-zero synthesized data were also considered to be too speculative to be

included in the time-series analysis.

Single Station Rainfall Trends

53. Stations selected for time-trend analysis were severely limited by data availability, data

continuity, durations of data collection, and geographic proximity to basins of interest. In order

of usefulness, the chosen stations were 1216, 1408, 1110 and 911. The monsoon season was

taken as May-September.

a. Station 1216 Siraha

Figure 11: Face-value means of annual and monsoonal variation + second order trend (station 1216 Siraha)

54. Note that the raingauge record for station 1216, Siraha, near the lower end of the

Gagan basin (project basin Nr 10) is the longest available within the Terai part of the project

area. It was selected for detailed study because of the data continuity (Annex 1) and long

duration of record. Years for which there were significant gaps in the record have been filtered

0

500

1000

1500

2000

2500

1945 1955 1965 1975 1985 1995 2005 2015

An

nu

al &

Mo

nss

on

al t

ota

ls, m

m

Year

Station 1216 Siraha - Annual and Monsoonal Totals (N=66)

Ann Tot

Mon Tot

y = 0.0026x2 - 7.4549x + 6023.1

R² = 0.02



out and were not included in the following calculations. A second order regression of just the

monsoon rainfall indicates: �� = . �2 − . � + .

55. Where RF is the total monsoon rainfall (taken as May to September), and Y is the year.

56. This is an almost linear trend of a slight increase in rainfall over time. However, note

that the ‘R2’ is only 0.02, indicating poor statistical significance, in this case heavily skewed by a quasi-7-year cyclicity of poorly quantified outliers. In the absence of anything better we

have no alternative but to accept this trend as real, but with the caveat that the 95% error

bounds substantially exceed the trend itself, and hance the trend must be regarded as

provisional.

b. Station 1408 Damak

57. Station 1408 supports 45 annual data points, as opposed to station 1216s record of

66 effective station years. At face value, the trend for #1408 is that of a very gradual decline

in rainfall over time. However, the R2 is again very low, at 0.0079. Moreover, the slight

decrease in rainfall disappears completely, yielding a completely flat (zero) trend if we adjust

just the single outlying point of 1971. Therefore, one must conclude that this station rainfall is

statistically invariant over time.

Regression with the 1971 outlier: RF = -0.0517Y2 + 202.64Y – 196494 R2= 0.0079

Regression without the 1971 outlier: RF = -0.1579Y2 + 628.19Y - 622498 R2= 0.0047

Figure 12: Face-value means of annual and monsoonal variation + second order trend (station 1408 Damak)

y = -0,0517x2 + 202,64x - 196494

R² = 0,0079

500

1000

1500

2000

2500

3000

3500

4000

4500

1960 1970 1980 1990 2000 2010

An

nu

al a

nd

Mo

nso

on

al T

ota

ls, m

m

Year

Station 1408 Damak - Annual and Monsoonal Totals (N=45)

annual total monsoon total



c. Station 1110 Tulsi

58. The record from station 1110 has 1.4% missing data, in which the gaps have been

interpolated, as far as is possible, from the data of station 1113. The monthly totals from April

1960, August 1962, August 2002 and July 2006 are possible underestimates. In addition, the

annual and monsoonal totals of 1962 (3503 and 2881 mm respectively) are extreme outliers

of almost double the normal range, suggestive of non-stationarity of the data set.

59. The data do not justify a second order regression, but if the 1962 extreme data are

ignored on the grounds of anomalous process, then a slight linear increase in monsoon totals

takes the form of:

RF = . Yr −

Figure 13: Face-value means of annual and monsoonal variation + second order trend (station 1110 Tulsi)

y = 2.8949x - 4280.1

R² = 0.02320

500

1000

1500

2000

2500

3000

1960 1970 1980 1990 2000 2010

An

nu

al a

nd

Mo

nso

on

al T

ota

ls, m

m

Year (excluding anomalous 1962)

Station 1110 Tulsi - Annual and Monsoonal Totals (N=58)

annual total

monsoon total



d. Station 911 Parwanipur

Figure 14: Face-value means of annual and monsoonal variation + second order trend (station 911 Parwanipur)

Table 12: Summary of monsoon rainfall trends from long-period, low-fragmented station records

Station Nr Trend Order Mean gradient � R2

1216 RF = 0.0026Y2 - 7.4549Y + 6023 2 +26.5 0.020 1408 RF =-0.0517Y2 + 202.64Y – 196494 2 0.0 to -28.0 0.008

1110 RF= 2.8949Y - 4280.1 1 +27.8 0.023

911 RF = -2.202Y + 5791.1 1 -27.8 0.004 ∇ mm per decade

60. NB: Do not confuse two similar tables: Table 10 is for the trend in net monsoonal rainfall

and Table 13 is for the trend in 24-hour maxima.

61. The overall conclusion is that, if climate change is influencing either the annual or

monsoonal rainfall totals, then the effect is scarcely, if at all, discernible as of 2014. No trend

is sufficiently robust to warrant high credence, although as a matter of caution, it would be

prudent to assume that the highest trend, of station 0911, provides a plausible extrapolative

basis for planning purposes up to at least 2020, and perhaps to 2030.

62. It is noted that none of the evidence–based instrumental (historical) trends are

consistent with any of the current AOGCM forward trends for 2020 and beyond.

e. Annual 24-Hour Maxima

63. The four stations with minimal missing data, and for which data are available up to

2013 or 2014, have been analysed for annual maxima (not peaks over threshold) to investigate

whether there is any discernible trend in the maximum daily rainfall. These results are plotted

y = -2.202x + 5791.1

R² = 0.0042

0

500

1000

1500

2000

2500

3000

1965 1970 1975 1980 1985 1990 1995 2000 2005

An

nu

al &

Mo

nss

on

al t

ota

ls, m

m

Year

Station 0911 Parwanipur - Annual and Monsoonal Totals (N=37)

annual monsoon monsson trend



below in Figure 15 to Figure 18. All these time-series are skewed by occasional outliers,

resulting in very low R2 values.

64. It is also noted that identical and consecutive annual 24-hour maxima occur more

frequently that would be expected by chance alone.

Table 13: Summary of monsoon rainfall trends from 24-hour maxima, low-fragmented station records

Station Years Trend Order Mean gradient � R2

1408 50 RF = -0.457Y + 1084.5 1 -4.4 0.014

1216 68 RF = 0.1355Y - 131.62 1 +8.1 0.003

1110 57 RF = 0.0018Y + 127.97 1 0.0 3E-7

0911 47 RF = 0.569Y - 994.89 1 +6.0 0.018 ∇ : mm per decade

65. Clearly there is no consistency of trend, and with such a small sample of stations (with

good record) it is impossible to draw firm conclusions. Ten other stations, with around 5%

missing data yield an apparent mean gradient of +0.7 mm per decade (standard deviation of

8.6 mm).

Figure 15: Annual 24-hour maxima (station 1408 Damak)

y = -0,457x + 1084,5

R² = 0,0142

0

50

100

150

200

250

300

350

400

1960 1970 1980 1990 2000 2010

Ma

xim

a, m

m

Year



Figure 16: Annual 24-hour maxima (station 1216 Siraha)

Figure 17: Annual 24-hour maxima (station 1110 Tulsi)

y = 0.1355x - 131.62

R² = 0.0031

0

50

100

150

200

250

300

1945 1955 1965 1975 1985 1995 2005 2015

Ma

xim

a, m

m

Year

y = 0,0018x + 127,97

R² = 3E-07

0

50

100

150

200

250

300

350

1955 1965 1975 1985 1995 2005 2015

Ma

xim

a, m

m

Year



Figure 18: Annual 24-hour maxima (station 911 Parwanipur)

66. The above four stations were, statistically, the best available, but since there was no

consistent trend amongst these four, the process was repeated for a total of 17 stations for

which a reasonably long record was available. Figure 19 shows the location of the stations.

Figure 19: Location of stations for trend analysis

y = 0,569x - 994,89

R² = 0,018

0

50

100

150

200

250

300

350

400

1965 1975 1985 1995 2005 2015

Ma

xim

a, m

m

Year



Table 14: List of stations for trend analysis

Station Name District

209 Dhangadhi Kaliali

405 Chisapani (Karnali) Bardiya

414 Baijapur Banke

501 Rukumkot Rukum

507 Nayabasti Dang

510 Koilabas Dang

706 Dumkauli Nawalparasi

716 Taulihawa Kapilbastu

910 Nijgadh Bara

911 Parwanipur Bara

912 Ramoli Bairiya Routahat

1107 Sindhuli Gadhi Sindhuli

1110 Tulsi Dhanusa

1216 Siraha Siraha

1319 Biratnagar Morang

1407 Ilam Tea Estate Ilam

1408 Damak Jhapa

67. These results are summarised in Table 15, below, and the spread of trend data

depicted histographically in Figure 20.

Table 15: Result of trend analysis

Station ID Station name R2 Gradient [mm/decade] Years Range

716 0.199 -20.3 38 71-09

501 0.192 -10.9 47 57-09

1107 0.005 -5.8 52 55-07

1408 0.014 -4.4 50 63-13

1319 0.006 -3.3 42 68-10

1407 0.004 -1.5 53 56-09

912 0.000 -1.3 53 56-09

414 0.000 -0.8 37 71-08

1110 0.000 0.0 57 56-13

405 0.005 +2.6 47 63-09

510 0.009 +3.7 38 71-09

910 0.009 +4.2 55 55-09

911 0.018 +6.0 47 67-14

1216 0.003 +8.1 68 47-13

209 0.085 +9.3 54 56-10

706 0.041 +10.3 37 71-08

507 0.170 +16.3 38 71-09

Average 0.7



68. These results point to an apparently Gaussian spread of 24-hour rainfall trends.

Moreover, the average trend is a trivial increase in maximum rainfall of only 0.7 mm per

decade. This amount is completely eclipsed by the following errors:

(i) Instrumental undercatch

(ii) Fragmentation of data

(iii) Bias from shortness of record

(iv) Bias from variation in start date

69. In other words, as of 2014, there is absolutely no evidence of climate change beginning

to have any impact upon rainfall intensity within the Terai and Siwalik terrains of Nepal. This

does not mean that no such trend will emerge in future, but that, currently, there is no

instrumental basis for incorporating increased 24-hour intensities in flood calculations.

Figure 20: 24-hour maxima Rainfall Trends: 17 stations in the Terai and Siwaliks

70. This yields the rainfall projections as in the second row of Table 16.

Table 16: Terai Rainfall Projections (monsoon season) based upon instrumental trends

Year 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060

Second order projection 1279 1318 1360 1404 1452 1502 1556 1613 1672 1734

Deviation from pre 1995 mean 36 75 117 161 209 259 313 370 429 491

% Deviation from pre-1995 μ +2.9 +6.0 +9.4 +13.0 +16.8 +20.9 +25.2 +29.7 +34.5 +39.5

71. In Table 16 the early values of the second order projection are taken as a plausible

upper bound, with the more likely linear trend showing effectively no change. The shaded

area, highlighted in italics, are regarded as implausible, even for upper bounds.

-25,0

-20,0

-15,0

-10,0

-5,0

0,0

5,0

10,0

15,0

20,0

Ra

infa

ll T

ren

d in

mm

pe

r d

eca

de

Stations



Inter-Study Comparison

72. A seeming difference in conclusions is found between this study and a previous study

by Baidya, et al (2008). Most of these differences arise from the differing set of stations used

in the statistical samples, and to a lesser extent because of the differing time-frame. Before

comparing the respective findings it is necessary to identify the essential differences of the

respective studies, as listed below in Table 17.

Table 17: Comparison of data in two studies

Time frame Number of raingauge stations

Start/end Total years Total Terai Siwaliks Himalayas

This study 1947/71-2014 38 to 67 23 15 7 1

Baidya, et al. 1961 to 2006 46 26 3 3 20

73. The essential conclusion of the Baidya study was that a significant increase in rainfall,

attributed to climate change, was occurring over the whole of Nepal. At first glance this is

inconsistent with the findings of this study, in which no significant trend in rainfall could be

detected. In detail, however, the two findings are rationally reconcilable in view of the following

factors:

(i) The selection of stations in this study is deliberately, and very strongly biased towards locations within the Terai and Siwaliks in order to correspond with the selected basins of the flood analysis, and hence the area of this study is markedly south of, and lower than, the area chosen by Baidya et al.

(ii) Baidya et al also noted in their study that the increase in rainfall was largely concentrated in the Himalayan mountainous areas, at intermediate altitudes, between about 1000 and 1500 metres. There was no obvious trend at higher than 1500 metres.

(iii) The Baidya et al study used data up to 2006. Since then the many of the stations have exhibited a very obvious decrease in rainfall, such as those indicated in Figure 15. This decrease has, in many cases, negated the former increasing rainfall trends.

74. In view of the above, we conclude that there is no appreciable climate-change trend

within the Terai and Siwaliks. However, it is logical that as global warming progresses, there

will be an increase in air temperatures (consistent with Baidya et al’s instrumental observations), particularly at night, over the mid-elevation mountainous areas of the Nepalese

Himalaya. This will result in a ‘carry-over’ of increased precipitable moisture, probably over the entirety of the mid-elevation region from west to east. Correspondingly, it is plausible to

expect that those river basins with a large fraction of their area in the mid elevation zone, will

experience a significantly increased peak rainfall and runoff. Hence the project river basins

which can expect a significantly increased rainfall are the West Rapti, Narayani and Karnali.

75. Conversely, the smaller river basins, whose area is entirely or mostly within the Terai

and Siwaliks, at less than 1000 metres elevation, can expect no increased rainfall trend, or

even a decreased rainfall trend.



76. We very strongly emphasize that all such trends are on too small a scale to be

resolvable on any existing GCM or downscaled derivative, and hence support for these

conclusions should not be sought from modelling. On the contrary, improved resolution of the

differing rainfall trends will only be discernible from good-quality evidence-based instrumental

monitoring.

77. The question of ‘how much more rainfall’ can be attributed to climate change in the mid-elevation region is currently impossible to answer with any degree of assurance, in part

because the models are insufficiently precise, and in part because, in this study, the post-2006

mid-elevation station data were either too fragmented or not available.

78. The Baidya et al study indicated an increasing rainfall trend averaging about 2.3

mm.year-1, with 81 stations yielding a positive trend, and 14 basins yielding a negative trend.

This is equivalent to about a 5.0 % increase in rainfall, relative to 2006, by 2045 (mid-range of

the DHM modelling period). Taking the post-2006 station rainfall data into account shows that

this estimate is probably too high, in which case the DHM forward projection, of a 3.0% to

4.0% but may, perhaps, be taken as a conservative upper bound.

24-Hour Maximum Rainfall

79. The few long-period stations to have a more or less unbroken record were examined

by decade to ascertain whether there was any significant trend in the frequency of 24-hour

intense rainfalls. These are summarized in the graphs of Figure 21 to Figure 23. No

consistent change in the frequency of intense rainfall was found. However, this is a statistically

small sample. Given a more complete data set, particularly with respect to 2009-2015 data,

and some higher elevation stations, it would be worth extending this exercise. The Baidya et

al study was for annual data trends. However, If the Baidya et al analysis remains true up to

2015, then there might be an expectation of no change in the 24-hour maxima at lower

elevations (i.e. a homogenous data-set), but a noticeable increase in the 24-hour rainfall

maxima at altitudes higher than 1000 metres.

Figure 21: 24-hr Rainfall Frequencies: Station 405, Chisapani Karnali

0

20

40

60

80

100

120

140

160

1966-75 1976-85 1986-95 1996-05 2006-15

de

cad

al

fre

qu

en

cy

≥ 5 mm ≥ 7 mm ≥ 9 mm ≥ mm ≥ 5 mm



Figure 22: 24-hr Rainfall Frequencies: Station 911, Parwanipur

Figure 23: 24-hr Rainfall Frequencies: Station 1216, Siraha

0

20

40

60

80

100

120

1966-75 1976-85 1986-95 1996-05 2006-15

de

cad

al

fre

qu

en

cy

≥ 5 mm ≥ 7 mm ≥ 9 mm ≥ mm ≥ 5 mm

0

10

20

30

40

50

60

70

80

90

1946-55 1956-65 1966-75 1976-85 1986-95 1996-05 2006-15

de

cad

al f

req

ue

ncy

≥ 5 mm ≥ 7 mm ≥ 9 mm ≥ mm ≥ 5 mm



III. RAINFALL INPUT FOR BASIN-RUNOFF MODELLING

80. It is evident from initial modelling results that the conventional distribution of

representative areas using ‘Thiessen polygons’ may be appropriate for relatively uniform-

rainfall areas, such as catchments largely or wholly within the Terai, but is wholly inadequate

to represent rainfall within mountainous terrains such as the Siwaliks and Lower Himalayas.

There are three reasons why the rain-data within the Siwaliks are skewed towards lower than

actual values:

(i) In most river basins the logistics of instrumental maintenance result in a preponderance of raingauges in valleys and low-gradient areas, where markedly lesser rainfall is experienced.

(ii) The raingauge network density is higher at lower elevations than in the catchment boundary areas.

(iii) The winds are stronger at higher elevations, and particularly around watershed boundaries (turbulent vortices), resulting in a greater instrumental under-catch.

81. Accordingly, some attempt at a more realistic rainfall input to the catchment models

was required. As already indicated in Figure 2, the rainfall altitude relationship is poorly

quantified, and lacks calibrated process studies to characterise the various physiographic

influences. An attempt to optimise a rainfall-altitude relationship using 3rd and 4th order

equations proved unsatisfactory. Therefore, we have adopted very approximate empirical

growth factors for different altitude ranges, relative to the rainfall at 1000 metres. These are

given in Table 18. In practice we regard the growth factors of ‘B’ and ‘C’ (3rd and 4th lines of

Table 18) as yielding plausible lower and upper bounds, respectively.

Table 18: Adopted mean rainfall-altitude relationships, normalised with respect to the rainfall at 1000 metres.

Altitude, (metres): 1000 1250-1750 1750-2250 >2250

A: 4th order regression 1 1.08 1.14 1.05

B: moderate R-A gradient 1 1.05 1.25 1.03

C: steep R-A gradient 1 1.3 1.4 1

Mean of B and C 1 1.18 1.33 1.02

82. Table 18 is for the seasonal or annual rainfall-altitude relationship. For flood analytical

purposes it is more useful to consider the 24-hour rainfall-altitude relationship. Even more

useful would be even shorter period rainfall time steps, but these data will not become

available until the current and proposed pluviometers data have built up a sufficiently long

time-series to be useful.

83. The 24-hour maxima have been plotted in Figure 15 to Figure 18.

84. We acknowledge that these factors constitute a crude approach to correcting the input

rainfall for modelling. This is regarded as provisional pending a more scientifically robust

method of assessment. In respect of the latter, the requirements for flood estimation, water

resources evaluation and hydroelectric planning, all underscore the need for detailed



hydrologic studies. The requirement, which is far beyond the scope of this project, is for an

intensively instrumented experimental catchment, and data collection for at least a decade

with a view to developing a multi-variate expression of the form: �� = . � 2 + . � + . � + . � + . + .

85. Where RF is the rainfall, a, b, c, d, e and f are empirical constants, Alt is the altitude

assuming a second order relationship at least up to 2000 metres, Tr is the quantified trend

parallel to the mountain front, Asp is the quantified aspect, Bar is the quantified barrier effect,

and Car is the quantified ‘carry-over’ effect.

86. We emphasize that whilst the development of such an expression is entirely an office

activity, the data required will require a major and sustained input of field-work. There are no

short cuts, and this data cannot be measured by remote sensing. This is beyond current

institutional capabilities, and will require donor input.

87. It has been suggested that the rainfall in mountainous areas be estimated upon the

basis of isohyetal mapping. Apparent isohyetal maps already exist, (ICIMOD, 1996; Dhar &

Nandargi, 2006) but we strongly emphasize that such maps are currently ill-founded and highly

misleading for the following reasons:

(i) As already discussed, the raingauge network upon which the isohyetal maps are based, is sub-optimal.

(ii) The isohyets are generated by a contouring package which takes no account of the all- important physiographic controls.

(iii) The raingauge data is treated as a homogeneous subset of the greater set of homogeneous data. This gives rise to such anomalies as concentric isohyets distributed around point-rainfall maxima and minima. These are ‘ghost contours’ which are purely artefacts of the contouring algorithm, and bear no relation to reality.

88. It will be possible to generate a realistic isohyetal map if, and only if, the multi-variate

approach is adopted. In the interim, the provisional (and inaccurate) isohyetal maps could, in

principle, be greatly improved by combining the data with precipitation radar reflection data.



IV. CONCLUSIONS

89. There is a general perception that climate change will result in increasing rainfall over

time, whether considered as increased frequency of 24-hour extremes, monsoonal or annual.

In this study we conclude that the changing rainfall distribution is more complex. The main

conclusions are as follows.

(i) The evidence-based instrumental record for the Terai and Siwaliks region, up to 2014, indicated neither increased frequency of high rainfall events, nor any increase in overall total rainfall (monsoon or annual). Local positive rainfall anomalies are almost perfectly balanced by an equal number and intensity of negative rainfall anomalies.

(ii) On the basis of the above there are no climate-change grounds for increasing the model rainfall inputs for those basins which are entirely within the Terai or lower Siwalik areas.

(iii) Climate models for those river basins entirely within the Terai and Siwaliks, also indicate a net decrease in rainfall over time. This is true irrespective of the cluster of models used, for all scenarios, for all forecasted time periods, and regardless of west-east location along the mountain front.

(iv) For basins with a substantial area within the mid-altitude range, north of the Siwaliks, and typically up to about 1500 metres (maybe up to 2000 metres in some places) there is a dramatically different climate change outcome, in which the rainfall greatly increases over time. We estimate this increase to vary between 15% and 62%, (see Table 10).

(v) We cannot prove, but strongly suspect, that a single causal mechanism of increasing mean air temperatures, accounts for both of the above trends. Higher temperatures along the Terai will somewhat suppress precipitation at lower elevations, but will carry over a greatly increased precipitable moisture to higher, more northerly parts of Nepal, including some of the upper basins of this flood study. This is consistent with the findings of this study, that of Baidya et al (2006), and with the CMIP5 modelling results (both the means and medians of 16 models).

(vi) In view of the above we recommend that climate-change weightings, as specified in Table 10 (as upper and lower bounds), be applied to the model rainfall inputs for the basins Karnali, Narayani, West Rapti, and Kankai for the fraction of the basins that is north of the Siwaliks.

(vii) Quite apart from climate change trends there is a further adjustment that is requires in the Karnali, Narayani, West Rapti and Kankai basins to account for the rainfall increase with altitude up to about 1500 metres (maybe up to 2000 metres). These adjustment factors, listed in Table 18, are somewhat crude and in need of improvement through rainfall-physiography process studies. Despite the need for caution in this relationship, we believe that elevation-range adjustments will be a marked improvement on the previous ‘Thiessen polygon’ methodology.

(viii) Within the Terai and Siwaliks the elevation adjustment is likely to be small, in which case the rainfall input may continue to be estimated on the basis of Thiessen polygons.



V. REFERENCES

Baidya S, Shrestha M.L, and Sheikh M.M., (2008). Trends in Daily Climatic Extremes of

Temperature and Precipitation in Nepal. Journ. Hydrol. and Meteorology. 5, (1), 38-51.

Bin Wang (2006). The Asian Monsoon. 791pp. Springer Verlag Chalise S.R., Shrestha M.L, et al (1996).Climate and Hydrological Atlas of Nepal. ICIMOD.

262pp. Dhar O.N and Nandargi S (2006). Areas of heavy precipitation in the Nepalese Himalayas.

Weather, 60, (12), 354-356. Lopez M/G. et al, (2015). Location and Density of Rain Gauges for the Estimation of Spatial

Varying Precipitation. Geografiska Annaler: Ser. A Physical Geography. 97, (1) 167-179.

Marahata S., Dangol B.S., and Gurung G.B., for ‘Practical Action Nepal Office’ (2009).

Temporal and Spatial Variability of Climate Change over Nepal (1976 - 2005). 76pp. https://practicalaction.org/file/region_nepal/ClimateChange1976-2005.pdf

Shrestha, D., Singh P., and Nakamura K (2012). Spatiotemporal variation of rainfall over the

central Himalayan region revealed by TRMM Precipitation Radar. Jour. Geophys. Res. 117, D22106, doi:10.1029/2012JD018140, (14pp).

Singh P and Singh V.P, (2001). Snow and Glacier Hydrology. Kluer Academic. 742pp. Timalsina P. (2014). Development of Intensity Duration Frequency Relationship of Rainfall

extremes and Regionalization. M.Sc. thesis, 99pp; Tribhuvan University, Institute Of Engineering.

Volkman TH.M. et al (2010). Multicriteria design of rain gauge networks for flash flood

prediction in semiarid catchments with complex terrain. Water Resources Research 46, W11554, doi:10.1029/2010WR009145, 16pp.

Westra S, Alexander L.V. and Zwiers F.W. (2013) Global Increasing Trends in Annual

Maximum Daily Precipitation. http://envsci.rutgers.edu/~toine379/extremeprecip/papers/westra_et_al_2013.pdf

https://practicalaction.org/file/region_nepal/ClimateChange1976-2005.pdf

http://envsci.rutgers.edu/~toine379/extremeprecip/papers/westra_et_al_2013.pdf



ANNEX 1: MONTHLY RAINFALL SUMMARIES



Table 19: Monthly rainfall summary for Damak, Station Nr. 1408

Unusable monthly totals, due to incomplete data, are shown in orange. Other incomplete months were synthesized to completion by interstation correlation / interpolation.

1956 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YEAR MONSOON

1963 0.0 0.0 13.0 39.4 181.6 408.2 437.1 565.4 292.2 176.4 28.0 0.0 2141 1885

1964 0.0 0.0 0.0 29.6 110.6 244.8 1012.8 492.2 316.2 78.2 0.0 0.0 2284 2177

1965 0.0 5.4 42.4 67.0 57.8 345.1 650.7 927.5 84.2 17.8 21.9 0.0 2220 2065

1966 0.0 6.0 0.0 8.4 112.2 268.4 583.6 1685.9 178.3 12.4 5.2 0.0

1967 0.0 0.0 40.0 70.8 257.8 514.2 1633.5 386.4 425.4 172.4 0.0 0.0

1968 63.8 0.0 0.0 9.6 37.6 271.0 551.7 505.6 609.0 473.2 12.4 0.0

1969 9.6 14.6 0.0 16.1 141.4 539.0 896.9 534.3 66.3 0.0 0.0 0.0

1970 14.6 17.4 73.5 59.8 267.3 714.9 811.1 690.0 209.0 0.0 0.0 0.0

1971 0.0 0.0 88.7 491.1 523.8 949.0 881.1 521.8 468.8 437.4 16.2 0.0 4378 3345

1972 0.3 26.4 10.4 90.2 94.2 418.3 596.6 235.8 276.8 24.0 1.2 0.0 1774 1622

1973 24.8 0.0 9.2 46.2 151.8 795.8 480.8 491.6 230.8 198.0 8.0 0.0 2437 2151

1974 29.2 0.0 45.2 22.8 241.8 438.2 1106.0 563.4 435.0 195.4 0.0 1.2 3078 2784

1975 4.8 12.0 0.8 18.4 201.2 804.2 651.1 284.8 637.2 87.6 0.0 0.0 2702 2579

1976 15.2 12.4 2.0 85.2 121.2 523.7 359.0 671.2 159.4 128.3 0.0 0.2 2078 1835

1977 0.0 5.0 36.2 153.9 261.9 280.2 438.9 610.0 273.5 195.0 143.5 21.0 2419 1865

1978 3.7 8.3 23.8 77.3 140.6 234.8 672.4 248.8 243.4 16.3 19.9 10.0 1699 1540

1979 8.0 56.5 0.7 56.1 68.0 317.0 933.8 690.0 548.5 228.7 105.5 24.8 3038 2557

1980 0.0 6.2 22.8 9.5 185.2 293.8 388.4 384.2 351.3 68.1 0.0 0.0 1710 1603

1981 14.6 23.3 46.3 126.8 165.5 324.3 1158.6 410.0 316.1 0.0 0.0 0.0 2586 2375

1982 0.0 8.3 5.6 58.1 54.0 368.0 670.0 78.9 472.1 78.3 29.8 0.0 1823 1643

1983 11.7 7.3 9.1 30.0 198.1 549.5 536.0 352.9 212.6 60.2 0.0 21.9 1989 1849

1984 40.0 31.6 14.3 42.3 139.2 488.4 808.9 332.6 848.1 243.6 0.0 16.5 3006 2617

1985 1.0 0.0 67.1 399.5 502.8 735.8 142.1 295.4 221.5 13.5 46.0 0.0 2425 1898

1986 0.0 0.0 2.0 40.0 182.8 355.8 423.1 457.0 319.4 132.5 0.0 5.0 1918 1738

1987 5.0 11.5 32.6 80.5 48.5 470.6 569.2 1098.0 470.6 193.5 0.0 0.0 2980 2657

1988 5.0 42.5 90.5 117.2 225.0 174.3 638.6 565.0 274.7 36.7 20.5 3.4 2193 1878

1989 10.0 47.5 12.0 0.0 314.5 573.5 1002.2 528.2 751.8 84.3 6.0 0.0 3330 3170

1990 0.0 28.5 76.2 115.5 315.8 511.4 482.3 616.6 324.0 78.0 0.0 11.0 2559 2250

1991 0.0 0.0 4.5 26.4 71.7 547.8 319.2 628.8 642.8 84.5 0.0 4.3 2330 2210

1992 2.0 0.0 0.0 5.1 144.0 185.9 765.7 307.1 266.8 113.0 0.0 15.1 1805 1670

1993 20.1 9.2 15.4 34.8 203.8 254.4 556.9 544.3 178.0 225.2 28.2 0.0 2070 1737

1994 68.4 69.4 42.2 25.8 121.0 280.9 304.2 521.2 152.5 0.0 0.0 0.0 1586 1380

1995 0.0 0.0 21.2 125.6 317.6 507.7 671.3 524.4 356.6 159.9 99.2 15.3 2799 2378

1996 18.7 10.9 0.0 15.4 275.4 397.4 1177.4 578.9 242.7 74.2 0.0 0.0 2791 2672

1997 0.0 10.8 18.0 66.9 133.4 369.8 428.3 284.9 674.0 0.2 4.4 54.5

1998 94.2 5.4 85.5 122.1 152.3 565.0 936.3 775.3 157.0 142.9 22.9 0.0 3059 2586

1999 0.0 0.0 0.0 12.8 245.1 413.7 809.9 794.2 382.1 66.1 16.9 0.0 2741 2645

2000 21.0 17.7 0.0 122.5 357.3 768.3 607.6 866.4 207.6 61.3 30.5 0.0 3060 2807

2001 0.0 0.0 16.3 79.0 174.6 179.1 320.5 398.5 657.5 388.6 11.5 0.2 2226 1730

2002 64.2 3.1 37.7 122.8 137.3 473.7 771.0 94.6 124.6 49.2 0.0 0.0 1878 1601

2003 29.9 15.2 28.9 53.4 109.8 428.8 727.2 253.6 231.0 102.5 0.0 69.5 2050 1750

2004 32.0 0.0 12.5 112.6 202.7 406.2 711.2 281.8 394.8 159.7 1.8 0.0 2315 1997

2005 12.3 5.0 80.9 60.9 77.8 235.6 246.0 748.1 122.4 68.6 0.0 0.0 1658 1430

2006 0.0 16.3 7.4 105.8 160.9 383.1 470.1 173.3 376.9 93.2 19.5 0.0 1807 1564

2007 0.0 78.7 9.4 115.0 157.9 561.0 656.8 356.6 370.0 86.0 0.7 0.0 2392 2102

2008 9.6 12.4 73.8 41.6 165.3 614.7 491.2 592.1 182.0 64.2 0.0 0.0 2247 2045

2009 0.0 0.0 25.0 41.8 309.5 488.2 373.2 798.4 133.4 178.3 0.0 0.0 2348 2103

2010 0.0 0.0 0.0 7.3 217.7 497.5 1194.8 468.0 566.3 115.1 44.3 0.0 3111 2944

2011 0.5 5.4 17.0 123.4 144.0 479.6 737.3 384.6 355.5 14.6 21.4 3.6 2287 2101

2012 3.0 0.2 3.0 97.1 171.2 610.7 387.6 248.2 266.8 87.0 0.0 0.0 1875 1685

2013 10.6 27.4 16.0 58.4 357.6 395.0 645.4 369.5 416.3 128.9 8.1 0.0 2433 2184



Table 20: Monthly rainfall summary for Parwanipur, Station Nr. 911


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YEAR Mon'n

1967 6 15.8 20 64.2 152.5 231.6 668.2 137.2 311.6 189.5 8 4.5 1809 1501

1968 9.5 18 3.3 20.4 28.5 296.6 604.6 219.7 282.6 34 10.5 22.7 1550 1432

1969 0 1.1 16 0.5 124 273.5 406.1 215.7 103.1 20 0 1.6 1162 1122

1970 51.5 6 18 91 170.5 272.3 685.3 317.5 426 0 14 0 2052 1872

1971 11.5 0.7 50.5 68 63 306.5 361.4 87.5 151 21 9 7.6 1138 969

1972 30.5 0 1 47 117.5 89.5 432.3 243.9 52.6 56.2 0 25.8 1096 936

1973 27.5 22 5.5 48.8 108.1 652.5 565.4 133.3 332.9 9 0 8.3 1913 1792

1974 6 2 6.3 4.4 72.2 333.8 481.7 430.8 612.5 118.4 5 36.6 2110 1931

1975 2 32.5 0 23.5 62 398.7 368.5 487.2 308.3 96.3 11.7 65 1856 1625

1976 1 8.8 13.3 83.4 42.7 125.7 824.3 582.9 132.9 139.4 0 4.5 1959 1709

1977 0 22.5 51.4 54 174.5 221.3 454.3 519.7 309.9 44.2 0 40 1892 1680

1978 0.5 21.2 18.1 0 131.1 157.1 491.1 171.3 192.4 33.5 2.2 5.3 1224 1143

1979 0 33.2 11 96.6 186.4 150.5 335.7 119.8 272.1 34.3 0 3.4 1243 1065

1980 33.5 1.3 16 8 99 512.1 201.7 598.4 227.1 23.5 0 17.2 1738 1638

1981 5 12.7 0 17.7 119.6 100.9 383.1 202.3 77.8 113.8 5.6 0 1039 884

1982 3.2 5.9 40.7 77.4 77.5 261.1 616.9 536.7 147.2 15.7 5.6 0 1788 1639

1983 41 24.5 18.2 4.7 82.6 128.4 246.9 226.9 326.1 40.5 0 0 1140 1011

1984 4.5 33 24.3 0 19.6 311.6 207.6 494 187.8 0 47.9 26 1356 1221

1985 23.3 0 31.2 21.2 190.4 397.3 271.1 182.1 102.2 0 0 20.3 1239 1143

1986 20.3 0 3.6 50.2 109.7 401.6 192.6 194.9 230.8 0 0 90.7 1294 1130

1987 13 0 54.1 26.6 99.6 175.8 972 535.6 291 57.8 20.3 0 2246 2074

1988 0 0 0 18.3 93.5 501.5 362.9 445 116.7 85.8 0 0 1624 1520

1989 17 6 0 87.6 259.5 297.9 588.9 358.2 131.7 0 0 0 1747 1636

1990 0 0 8.7 30.5 275.8 284.7 426 397.5 361.4 114.5 0 0 1899 1745

1991 50.2 31.6 2 31.2 195 323.8 630.6 143.2 352.3 17.8 19 19.2 1816 1645

1992 39.5 25 79.5 21.02 0.29 601.6 51.19 318 271.6 63.4 0 1 1472 1243

1993 15.8 0 0 98.2 110.7 612 557 158.4 214.1 75.4 4.6 0 1846 1652

1994 20.9 6.3 14.7 11.5 76.6 101 381.5 814.3 47.7 64.3 0 0 1539 1421

1995 0 0 6.9 79 121.4 333.4 358.3 118.8 261 124.6 0 27.9 1431 1193

1996 0 65.5 14 2 63 349.8 1065 487.8 283.2 59.2 1 0 2390 2249

1997 1.8 0 25.5 32.3 85.7 369.6 578.2 421.1 151.4 16.1 0 0 1682 1606

1998 0 0 0 7.8 120.2 137.8 402.5 694.4 55.3 41.3 0 12.6 1472 1410

1999 0 3.6 0 29.3 102.3 194.9 247.4 295.9 144.5 182.2 0 1.4 1202 985

2000 1.7 21.5 30.5 92.9 141 131.5 756.2 316.5 187.7 5.1 16.31 15.1 1716 1533

2001 0 7.6 5 40.2 14.8 224.8 0 215.3 0 1.2 0 0 509 455

2002 3.4 21.7 9 42.7 192.3 432.9 263.2 311.2 111.5 213.9 0 0 1602 1311

2003 11 18.5 0 1 263.5 228.5 255.8 464 211.6 130.1 0 7.81

2010 0.01 3.6 0 29.3 102.3 161.9 280.4 294.3 146.1 182.2 0 0 1200 985

2011 3.1 21.5 30.5 38.51 180.4 0 851.1 353.1 187.7 5.1 16.31 0

2012 15.11 7.61 5.01 40.21 14.8 214.8 0 194.3 0 1.21 0 0

2013 3.4 21.71 9 40.71 186.8 417.6 277 311.2 111.5 213.9 0 0 1593 1304

2014 11 18.2 0.3 0 264.5 227.5 255.8 464 210.8 130.9 0 7.81 1591 1423



Table 21: Monthly rainfall summary for Tulsi, Station Nr. 1110


1956 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YEAR MONSOON

1956 44.2 3.0 19.8 8.6 126.7 459.8 227.8 619.9 144.8 125.8 1.5 0.0 1782 1579

1957 4.6 0.0 0.0 0.0 37.6 171.6 377.4 459.2 268.7 0.0 6.9 0.0 1326 1315

1958 10.4 0.5 0.3 23.4 56.7 187.5 401.2 546.2 335.5 64.7 1.0 9.9 1637 1527

1959 8.6 0.0 41.4 58.8 46.7 285.3 73.9 82.5 165.2 111.7 0.8 0.0 875 654

1960 0.0 0.0 55.3 0.0 71.4 240.5 501.4 330.8 290.4 50.8 0.0 0.0 1541 1435

1961 2.4 27.9 0.0 8.1 92.5 369.7 254.1 356.2 87.5 59.8 1.8 2.8 1263 1160

1962 40.4 22.9 35.3 440.1 226.9 610.4 592.4 995.0 455.4 84.0 0.0 0.0 3503 2880

1963 7.0 0.0 123.0 226.2 77.6 496.9 261.6 430.8 267.0 6.4 37.0 0.0 1934 1534

1964 0.0 3.6 0.0 231.7 35.2 117.9 492.2 152.8 155.0 97.0 0.0 0.0 1285 953

1965 0.0 0.0 34.6 22.4 35.0 292.6 461.7 524.7 168.7 143.4 96.2 0.0 1779 1483

1966 78.4 0.0 0.0 6.8 70.6 158.9 409.5 853.8 156.4 52.8 0.0 12.6 1800 1649

1967 0.0 0.0 29.6 35.1 6.8 357.2 583.5 256.4 392.0 4.0 2.0 0.0 1667 1596

1968 31.6 0.0 9.2 12.2 37.1 389.0 355.6 132.4 232.4 102.2 0.0 0.0 1302 1147

1969 0.2 2.6 14.4 51.0 30.5 303.8 386.0 264.4 71.7 25.5 0.0 0.0 1150 1056

1970 11.8 35.2 7.6 25.0 100.4 419.4 527.1 275.0 272.3 36.0 0.0 0.0 1710 1594

1971 0.0 23.0 9.0 171.6 193.7 378.7 600.7 314.8 150.1 95.8 9.0 0.0 1946 1638

1972 19.6 23.8 32.9 6.0 70.7 261.9 606.9 305.9 99.7 72.0 6.6 0.0 1506 1345

1973 9.1 1.0 24.0 87.9 47.2 480.3 176.6 284.5 219.0 132.8 0.0 0.0 1462 1208

1974 0.0 0.0 21.2 6.4 121.0 140.0 615.6 563.0 297.8 105.6 0.0 5.2 1876 1737

1975 18.4 17.2 0.0 27.6 113.6 217.2 432.6 196.6 162.0 60.8 0.0 0.0 1246 1122

1976 14.0 9.6 0.0 24.0 140.8 374.8 320.4 250.4 192.0 36.0 0.0 0.0 1362 1278

1977 0.0 2.0 20.0 64.4 80.4 96.8 243.2 607.0 191.6 66.0 36.0 13.2 1421 1219

1978 5.2 4.4 19.8 143.0 61.5 339.2 438.0 147.0 249.5 148.0 4.8 3.7 1564 1235

1979 5.9 21.4 0.0 18.7 77.0 356.3 330.3 594.9 237.2 51.8 12.0 20.3 1726 1596

1980 0.0 0.0 35.5 11.2 100.4 198.1 381.0 311.7 349.2 32.8 0.0 0.0 1420 1340

1981 10.1 12.7 54.2 6.6 181.2 172.7 514.8 472.8 200.2 0.0 12.3 0.0 1638 1542

1982 4.3 2.1 44.3 43.5 89.3 479.5 354.1 167.9 345.9 37.0 31.2 0.0 1599 1437

1983 11.2 0.0 9.8 28.0 183.2 176.1 631.8 219.7 186.8 116.8 0.0 18.4 1582 1398

1984 23.2 8.2 8.1 64.9 197.8 407.4 728.6 235.8 628.7 17.4 0.0 2.4 2323 2198

1985 1.2 4.3 41.7 264.4 160.9 526.3 535.5 377.8 141.6 1.0 57.0 0.0 2112 1742

1986 0.0 8.9 0.0 40.0 273.2 141.9 215.1 459.7 252.6 36.4 2.3 29.1 1459 1343

1987 0.0 39.6 63.8 16.7 85.8 129.9 838.8 699.1 566.4 185.8 16.8 3.7 2646 2320

1988 0.0 66.6 39.2 124.0 105.4 310.2 468.2 522.8 417.4 28.7 12.3 16.3 2111 1824

1989 19.7 0.0 0.0 0.0 184.6 180.0 595.6 254.0 477.0 21.9 0.0 0.0 1733 1691

1990 0.0 31.7 24.5 73.3 160.2 268.0 537.7 327.1 340.7 48.6 0.0 0.0 1812 1634

1991 34.4 0.0 69.2 26.8 120.0 379.3 152.1 375.8 387.5 103.8 0.0 20.7 1670 1415

1992 8.3 12.3 0.0 37.2 101.9 117.7 399.0 177.2 244.6 99.9 0.0 4.2 1202 1040

1993 12.3 0.0 28.5 198.5 68.5 192.7 623.1 584.6 153.4 135.9 0.0 0.0 1998 1622

1994 59.6 19.8 13.7 34.8 57.9 158.3 377.3 275.3 305.7 20.0 0.0 0.0 1322 1175

1995 5.3 17.9 7.4 0.0 156.8 428.8 499.5 595.0 112.2 31.5 42.5 19.3 1916 1792

1996 15.3 3.0 0.0 12.3 79.6 458.1 717.7 239.2 232.9 80.2 0.0 0.0 1838 1728

1997 13.3 0.0 4.3 54.9 85.2 334.8 439.0 483.7 269.4 7.3 0.0 53.1 1745 1612

1998 3.4 5.3 34.8 37.0 54.1 162.7 912.6 556.3 171.4 53.3 32.6 0.0 2024 1857

1999 0.0 0.0 0.0 2.3 246.6 384.2 427.0 514.3 153.7 149.3 0.0 0.0 1877 1726

2000 5.7 27.8 4.2 46.0 150.1 251.4 342.0 456.5 79.7 24.5 0.0 0.0 1388 1280

2001 3.2 0.0 0.0 68.5 311.3 206.0 569.0 423.8 340.9 202.7 0.0 0.0 2125 1851

2002 36.5 12.3 0.0 108.1 154.8 187.6 908.4 109.4 320.7 150.4 2.2 10.5 2001 1681

2003 23.4 14.2 40.6 63.4 192.0 365.4 593.4 163.4 209.5 68.5 0.0 12.5 1746 1524

2004 13.8 0.0 0.0 99.3 82.9 297.2 1051.3 291.5 404.7 66.1 0.0 0.0 2307 2128

2005 25.3 6.7 48.5 65.1 103.7 181.3 195.2 682.7 37.7 113.2 0.0 0.0 1459 1201

2006 0.0 0.0 5.1 127.1 66.8 324.3 322.0 197.9 525.9 44.9 0.0 19.3 1633 1437

2007 0.0 59.6 14.2 36.8 76.4 304.4 646.1 377.7 353.0 68.7 21.4 0.0 1958 1758

2008 3.2 8.4 10.4 47.9 217.4 278.3 349.3 259.0 306.9 10.3 0.0 0.0 1491 1411

2009 0.0 0.0 8.5 0.0 170.8 4.3 324.4 236.7 195.5 25.1 0.0 0.0 965 932

2010 0.0 3.4 6.4 52.8 66.7 236.6 297.2 375.6 170.7 19.8 0.0 3.4 1233 1147

2011 0.0 14.2 13.4 18.0 148.8 203.2 559.2 235.2 381.3 31.4 38.6 0.0 1643 1528

2012 12.3 4.3 4.3 94.4 92.3 204.4 216.3 278.7 223.0 8.4 0.0 0.0 1139 1015

2013 6.4 39.8 0.0 64.3 249.8 293.2 164.6 231.3 239.5 119.1 0.0 0.0 1408 1178



Table 22: Monthly rainfall summary for Siraha, Station Nr. 1216


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

1948 0.31 0.29 0.31 0.3 0.31 0.29 479.7 216.8 194 48.7 0 0 939 891

1949 0 11.6 8.9 10.4 106 114.3 525.8 252 143 98.2 134.1 0 1404 1141

1950 22.4 51.7 0 89.4 243.9 473.3 225.2 262.4 165.9 154.5 0 0 1689 1371

1951 10.7 0 42.5 0 133.2 294.6 152.8 240.2 91.8 0 0 0 966 913

1952 1 0 9.4 17.8 73.5 476.8 259.5 271.7 150 35.6 13.7 0 1309 1232

1953 0 22.8 15.5 63.4 270.2 35.5 359.9 224.3 217 30.3 0 0 1239 1107

1954 26.2 0 4.1 1 91.5 262.6 459.2 191.3 244 2.8 0 0 1283 1249

1955 1 5.8 3.6 0 72.2 258.3 790.7 514.8 245.9 25.4 0 0 1918 1882

1956 0 0 1.5 32.1 82 235.6 816.6 220.6 105.4 29 0 0 1523 1460

1957 29.2 1 15.4 15.2 214.6 511.3 311.5 594 284.2 278.6 59.5 0.8 2315 1916

1958 88.7 1 2 1.5 8.1 237.3 250.6 398.3 42.7 39.4 0 5.6 1075 937

1959 35.3 1.3 0 46.8 58.4 262.4 279.4 1225 292.5 42.4 0 0 2243 2118

1960 147.9 0 21.3 33.5 12.2 169.5 117.4 60.6 225.2 104.2 0 0 892 585

1961 0 0 85.4 0 85.4 118.7 319.4 144 291 15.4 0 0 1059 959

1962 0 59.7 0 11.8 88 269.1 334.3 434.1 124.4 105.2 5.1 3.4 1435 1250

1963 18 14.7 14.4 13.4 58.1 196 84.5 567.8 99.4 161.6 0 10.2 1238 1006

1964 1.6 0 14.5 55.6 80 218.3 289.4 192.5 139.2 169.6 54.1 0 1215 919

1965 0 0 39 31.4 82 63.7 484.6 170.7 373.7 26.4 0 0 1272 1175

1966 0 0 47.4 7.3 39.2 340.7 317.9 563.7 148.4 100.2 18.5 0 1583 1410

1967 54.4 12 0 0 48.6 20.3 389.1 662 148.3 46.5 14.5 8.9 1405 1268

1968 0 0 63.3 32.5 39 244 360.6 100.2 205.5 41.2 0 0 1086 949

1969 35.8 0 9.6 0 60.4 213.8 248.8 341.9 85.5 0 0 0 996 950

1970 2.1 2.1 32.4 33.1 51.1 112 159 386.8 154.1 4.5 0 0 937 863

1971 0 12.3 0 10 53.4 334.4 422.2 263.8 189.7 36.8 0 0 1323 1264

1972 3.6 4.6 0.4 221.7 204.9 319.6 375.2 408.8 178.7 19.1 14.2 0 1751 1487

1973 0 26.7 16.6 1.2 28.2 245.4 300.5 100.1 332.3 65 12.8 0 1129 1007

1974 26.9 9.8 4 23.2 116.8 461.3 121.2 213.4 120.4 206 0 0 1303 1033

1975 4.8 1.5 34.6 43.8 65.1 185.5 623.3 446.5 282.5 35.6 0 0.5 1724 1603

1976 15.4 14.5 4.8 19.6 79 271.1 740.3 192.4 509.8 59.4 0 0 1906 1793

1977 25.7 54 0 18 145.4 302.6 241.3 533.4 102.6 20.2 0 0 1443 1325

1978 0 2.8 0 45.6 129.5 138.2 280.5 332.6 87.2 151.2 47.8 35.2 1251 968

1979 22.8 9.9 27.2 36 153.2 98.7 504.4 302.4 144.6 102.4 44.5 2.5 1449 1203

1980 20.4 19.6 3.2 41.3 5.1 170.5 455.7 106.9 305.1 68.4 14.4 3.8 1214 1043

1981 10.6 0 11.8 0 127.4 147 573.8 412.6 336.5 13.8 0 0 1634 1597

1982 21.4 41.7 23.7 74.5 211.4 248.8 493.8 239.7 101 0 0 0 1456 1295

1983 0 5.3 6.2 30.5 34.9 170.4 200.7 24.8 239.5 13.5 9.2 0 735 670

1984 21.2 0 3 45.4 113.1 159.1 467.8 249.7 196.8 53 0 18.4 1328 1187

1985 29.8 22 6.4 30.1 142.8 381.8 607.9 109.5 298 0 0 13 1641 1540

1986 0 0 0 149.5 182.3 391.9 227 219.6 78.2 21.3 27.2 0 1297 1099

1987 0 8.2 0 45.7 110.8 97.6 241.7 241 184.8 126.8 0 58 1115 876

1988 0 16.3 12 20.3 24.3 144.8 547.8 800.7 536 176.9 0 2 2281 2054

1989 0 83 40.8 78.2 88.1 190.3 659.3 443.8 135.5 26.2 1.8 26.1 1773 1517

1990 13.2 4.8 10.1 0 134.9 113.8 495.4 164.1 335.1 3 0 11 1285 1243

1991 0 28.3 28.1 49.6 215.4 173.9 365.1 316.7 382.5 15.4 0 0 1575 1454

1992 11.1 0 14.7 54.3 30.6 158.5 334.6 380.9 242.2 30.9 0 0 1258 1147

1993 0 1.2 0 25 169.8 64 303.7 121.1 42.01 111.9 1.5 0.01

1994 8 5.2 24.91 116.3 193.3 227.6 570 572.4 196.8 12.7 0 0

1995 31.41 28.7 3.4 4.52 26.8 73.02 365 429.6 233.8 0 0 0

1996 9.2 22.1 3.71 0 30.1 240.5 318.8 513.2 116.5 4.6 29.21 11.2

1997 26.7 5.5 0.01 9.1 16.32 295.7 489.3 329.9 53.2 104.2 0 0

1998 9.3 2.4 5.2 33.51 66.3 251.2 388.4 197.4 130.4 4.9 0 70.6

1999 6.1 7.4 13.3 65.2 60.2 245.1 673 528.5 66.3 99.6 19 0 1784 1573

2000 0 0 0 9.9 204.6 401.8 360.6 233.9 67.3 284.9 11.4 0

2001 0 16.4 0 62.2 127.6 506.3 282.2 203.2 123.1 32.6 0 1.4

2002 2.1 0 0.01 45.7 214.3 216.4 331.6 386.8 205.1 428.5 0 0

2003 47.11 12.4 3.2 54.6 152.6 159.9 900.3 157.6 147.7 7.4 0 0.16 1643 1518

2004 31 15.7 35.1 131.8 123.7 401.6 437.4 130.4 63.8 113.9 0 6.1 1491 1157

2005 43.4 0 0 81.6 203.8 399 770 157.9 172 47.5 0 0 1865 1693

2006 39.3 15 22.4 66.5 121.1 201.7 127.2 541.2 89.9 11.7 0 0 1875 1081

2007 0 0 3.6 63 152.4 125.2 271.4 120.5 534.5 78.6 0 11.3 1361 1703

2008 0 56.3 11.8 48.1 129.1 328.5 905.3 459.6 364.4 28.8 0 0 2332 2187

2009 2.1 0 7.3 51 138 195.6 248 309.8 58.1 7.6 0 0 1018 950

2010 0 0 0 18.2 230.6 116.7 277 189.9 0 0 0 0 832 814

2011 5.5 17.4 26.5 52.9 231.5 196.7 465.8 203.9 253.7 0 17.8 0 1472 1351.6

2012 12.9 2.2 3 40.5 13.4 269.3 252.8 181.2 113.9 24.8 0 0 914 842

2013 8.5 32.7 4.4 32.3 170.5 378.2 114.6 135.2 272.2 100.4 0 0 1249 1070.7

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