Consultant Report
Project Number: 45206-001 September 2020
Nepal: Water Resources Project Preparatory Facility Flood Forecasting and Early Warning System: Lakhandei Basin
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GOVERNMENT OF NEPAL
Miniistry of Energy, Water Resources and Irrigation
Department of Water Resources and Irrigation
WRPPF: Preparation of
Priority River Basins Flood Risk Management Project, Nepal
Flood Forecasting and Early Warning System: Lakhandei Basin
4 April 2019
Mott MacDonald 22 Station Road Cambridge CB1 2JD United Kingdom
T +44 (0)1223 463500 F +44 (0)1223 461007 mottmac.com
WRPPF: Preparation of 1243 124 124 C:\Users\Erik Klaassen\Documents\Work\01 Project\WRPPF - 383877 MM - Nepal\04 PriorityDeliverables\11 River FFEWS\Lak handei\1\190404Basins FFEWS Lakhandei.docxFlood Risk Management Mott MacDonald Project, Nepal Flood Forecasting and Early Warning System: Lakhandei Basin
4 April 2019
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Mott MacDonald | WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal Flood Forecasting and Early Warning System: Lakhandei Basin
Issue and Revision Record
Revision Date Originator Checker Approver Description 01 23/11/18 Iqbal C. Hetmank C. Hetmank 1st submission Hassan D. Ocio 02 04/04/19 Iqbal Peter Ede C Hetmank Final Submission Hassan A Akindiji Audrey Despinasse
Document reference: 383877 | REP | 0038
Information class: Standard
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Mott MacDonald | WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal Flood Forecasting and Early Warning System: Lakhandei Basin
Contents
Executive summary 3
1 Introduction 8 1.1 Project background 8 1.2 Problem statement 8 1.3 Understanding needs of FFEWS 9 1.4 Study area 9 1.5 River system in the basin 11 1.6 CDMA and GSM coverage in Nepal 14
2 Hydro meteorological data 16 2.1 Introduction 16 2.2 Hydro-meteorological gauge densities in Nepal and other countries 17 2.3 Existing hydro-meteorological network Nepal 17 2.3.1 Rainfall 17 2.3.2 Evaporation 18 2.3.3 Temperature 18 2.4 Water level stations 18 2.5 Discharge stations 18 2.6 Gridded Meteorological data 18 2.6.1 APHRODITE precipitation data 19 2.6.2 TRMM3B42 Precipitation 19 2.6.3 MODIS Snow Cover Data 19 2.7 Forecasted Meteorological data 20 2.8 Summary of availability of data 20
3 DHM and existing flood forecasting models 22 3.1 DHM’s mandate 22 3.2 Existing flood forecasting models in Nepal – an overview 23 3.3 Examples of operational flood forecasting models from other countries 23 3.4 Dissemination of forecasts 24
4 Flood forecasting modelling 25 4.1 Flood forecasting modelling framework 25 4.2 Objectives of flood forecasting modelling 26 4.3 Gauge-to-gauge correlation 27 4.4 Hydrological modelling 27 4.5 Routing modelling 28 4.6 Hydrodynamic modelling 28
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4.7 Modelling software 29 4.8 Modelling software comparative list 30 4.9 FFEWS cost consideration 30
5 Rain gauge network design 32 5.1 Introduction 32 5.2 Auto telemetry rain-gauge 32 5.2.1 Description 32 5.2.2 Time of observation 33 5.2.3 Operation and measurement 33 5.2.4 Data transmission, storage and archive 34 5.3 Radar rain gauge 34 5.3.1 Description 34 5.3.2 Specification 34 5.4 Rain gauge network recommended for installation 35 5.5 Budgets for proposed rain gauge network installation 37
6 Hydrometric network design 38 6.1 Water level gauge network 38 6.1.1 Description 38 6.1.2 Time of observation 39 6.1.3 Operation, measurement and maintenance 39 6.1.4 Data transmission, storage and archive 39 6.2 Discharge measurement station 39 6.2.1 Description 39 6.2.2 Discharge measurement equipment 39 6.2.3 Cableway flow measurement 40 6.2.4 Equipment budget for discharge measurement 41 6.3 Hydrometric gauge recommended for installation 42 6.4 Hydrometric gauging network budget 44
7 Topographic and asset survey 45 7.1 Topographic survey 45 7.2 Survey budget 46 7.3 Satellite imagery 46
8 Flood forecasting model development 47 8.1 Mathematical modelling 47 8.2 Rationale for different forecasting approaches 48 8.3 Gauge to gauge correlation 49 8.4 Hydrological modelling 51 8.4.1 Review of existing data and models 52 8.4.2 Catchment delineation 52
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8.4.3 Hydrological input: Rainfall, temperature and evapotranspiration 53 8.4.4 Bias correction 53 8.4.5 Calibration 53 8.4.6 Validation 54 8.5 Combined rainfall-runoff and gauge-to-gauge correlation 54 8.6 Pure 2-d modelling 55 8.7 1-d modelling 55 8.7.1 River network 55 8.7.2 Calibration and validation 57 8.8 1d/2d linked modelling 58 8.9 Operation of forecasting model 59 8.9.1 Key tasks 59 8.9.2 Real-time data transmission and maintenance 60 8.9.3 Existing forecast model operating system within DHM 61 8.9.4 Delft-FEWS 62 8.9.5 Dissemination of forecast 63 8.9.6 Data assimilation 63 8.10 Evaluation of forecast 64 8.11 Model development schedule 64 8.12 Model development budget 65 8.13 Person-months for experts 66 References 68 Appendices 70
A. Modelling software comparison 71
B. Comments and responses 74
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List of abbreviations
ADB - Asian Development Bank ASCE - American Society of Civil Engineers CBA - Cost Benefit Analysis CBDRM - Community Based Disaster Risk Management CDMC - Community Disaster Management Committee DDC - District Development Committee DDRC - District Disaster Relief Committee DEM - Digital Elevation Model DEOC - District Emergency Operation Centre DHM - Department of Hydrology and Meteorology DMF - Design and Monitoring Framework DoWRI - Department of Water Resources and Irrigation DPR - Detailed Project Report DWIDM - Department of Water Induced Disaster Management EARF - Environmental Assessment Review Framework EIA - Environmental Impact Assessment EIRR - Economic Internal Rate of Return EMP - Environmental Management Plan EPR - Environmental Protection Rule EWS - Early warning system FFEW - Flood forecasting and early warning FHRMP - Flood Hazard Mapping and Risk Management Project FIRR - Financial Internal Rate of Return FMA - Financial Management Assessment GDP - Gross Domestic Product GESI - Gender and social inclusion GFS - Global forecast system GIS - Geographic information system GLOF - Glacier Lake Outburst Flood GoN - Government of Nepal GPS - Global Positioning System ICIMOD - International Centre for Integrated Mountain Development IEE - Initial Environmental Examination IP - Indigenous People IPP - Indigenous People Plan IPPF - Indigenous People Plan Framework IRP - Involuntary Resettlement Plan IRPF - Involuntary Resettlement Plan Framework LDC - Least Developed Countries MoHA - Ministry of Home Affairs MoEWRI - Ministry of Energy, Water Resources and Irrigation MOUD - Ministry of Urban Development NAPA - National Adaptation Programme of Action NEOC - National Emergency Operation Centre NPR - Nepalese Rupiah NPV - Net Present Value OPEC - Organization of the Petroleum Exporting Countries
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PAM - Project Administration Manual PCP - ADBs Public Communication Policy PEOC - Provincial Emergency Operation Centre PEP - People’s embankment program PET - Potential evapotranspiration PMU - Project management unit PRA - Project Risk Assessment PSA - Poverty and Social Analysis RAH - Resettlement Affected Household RRP - Recommendation Report to the President RUDP - Regional Urban Development Project SDAP - Social Development Action Plan SDG - Sustainable Development Goals SMS - Short Message Service SPRSS - Summary poverty reduction and social strategy SPS - ADB Safeguard Policy Statement TOR - Terms of Reference UK - United Kingdom USD - Unites States Dollar VDC - Village Development Committee WC - Working Committee WECS - Water and Energy Commission Secretariat WRF - Weather research and forecasting WRPPF - Water Resources Project Preparatory Facility
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Executive summary
Overview of forecasting tools It has been proposed to develop a flood forecasting and early warning system (FFEWS) for Lakhandei basin. This includes a simple tool for use with the most advanced hydraulic models being employed in countries like Australia and UK. The simple tool could be operational within eight months or as soon as some hydrometric data becomes available from the proposed new gauge network Over a period of three years, advanced hydraulic models will be developed, calibrated, validated and will be made operational as more data becomes available from the new gauging network and new measurements. The following forecasting tools have been proposed:
● Gauge to gauge correlation: the simplest and cheapest method, fast to develop, and thus could be operational soon. However, forecasts have a very short lead time and this method is not appropriate in upper steep slope river reaches. There are also other limitations. ● Combined rainfall-runoff and gauge-to-gauge correlation: due to the addition of a runoff model, the forecast lead time could be up to 72hrs. However, this requires a stage-discharge rating curve at each gauging station; such a rating curve is difficult to develop for out of bank flow conditions without a hydraulic model. ● 1-d model: this tool will be developed for the entire river system in the Terai and is appropriate for flood forecasting; the same model type is used in Bangladesh. ● 1-d/2-d linked model: this will be the final delivery around month 24; the pure 2-d model and 1-d model will be transformed into a 1-d/2-d linked model; this is the advanced forecast model used in some areas of Australia, Malaysia and UK. Rationale for different forecasting approaches The four approaches, described above, are inter-linked and essential and/or complementing components to the final deliverable/ flood forecasting and early warning system FFEWS model, i.e. the 1-d/2-d linked FFEWS model. The rationale, advantages and disadvantages of each approach are described below:
Gauge-to-gauge correlation: the simplest and cheapest method. It is an integral part of data analysis; this tool will provide support to the other four components, and thus, could be an option to use as a quick forecasting tool. It can generate new knowledge, to be translated into the final deliverables (1d model and 1-d/2-d linked model). Advantages will be that CBDRM could be operational earlier and potential areas of uncertainty in flood level forecast could be identified. DHM is using this method in many of their river basins, e.g. in Karnali. This tool and expertise from DHM could readily be used in this basin with some nominal input from international consultant as the tool has to be customised for new basin, need for minor changes in code and parameters may be required and thus, international consultant’s input is considered. Thus, a minimum budget has been proposed for developing this tool (Table 1). If ADB and DoWRI prefer, this component could be dropped (cost for each tool has been shown separately for easier decision making). However, there will be a deployment time, in all six basins, for new hydro-meteorological data to become available, so this work is a good utilisation of the deployment time, as it generates the opportunity for transferring early knowledge to the final product.
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● Rainfall runoff model is the main input to all other components: a) gauge to gauge correlation, b) 1-d river model, c) pure 2-d model and d) 1-d/2-d linked model. Combining rainfall model with gauge-to-gauge correlation will increase the lead time (as in the rainfall forecast) up to 24, 48 and 72 hours. However, at the forecasting points, the discharge vs water level rating curve shall be required so that forecasted runoff can be converted to water level using the rating curve. The rainfall runoff model provides inflows from the upper catchment and distributed inflows from intermediate catchments to 1-d, 2-d and 1-d/2-d linked model. The accuracy of the flow forecast depends on the accuracy of the rainfall forecast, which decreases with increasing lead-time. ● The 1d hydraulic model, as a standalone tool, can be applied as a forecasting tool once it is ready; without the 1-d model, a linked 1-d/2-d model (which is proposed as final deliverable) cannot be developed. Therefore, we have proposed to employ a 1-d model as forecasting tool as soon as it is ready. In any case, for certain reaches of the river, there will only be a 1-d model, as a 1-d/2-d linked model is not feasible to be developed for the entire reach of the river. This tool will also give useful feedback on forecasting performance, which then could be translated into the final deliverable. In summary, 1-d model development is not a duplicating tool; it is an essential pre-requisite. Should DoWRI and ADB decide not to take forward 1-d/2-d linked modelling, then a 1-d model will be the final product. This is the tool which DHM operate in the Bagmati, Koshi and West Rapti basins. The advantage of a 1-d model is that it runs efficiently, which is a key requirement for real time forecasting. However, a 1-d model does not have direct map output for flood risk or hazard; these require separate and customised GIS development, e.g., as practiced by forecast model in Bangladesh (http://ffwc.gov.bd/). Such a GIS tool is under development within DHM. It will need to be developed in this project in the 1-d only model reaches of the river. ● A 1-d/2-d linked model is the final deliverable; such FFEWS models are already in operation in countries like Australia, New Zealand, Malaysia and UK (Syme, 2007; Huxley, 2016). Therefore, we recommend developing this next generation of FFEWS tool; otherwise, by the time the project is complete, Nepal won’t fall behind on national standards. The 1-d/2- d linked model can forecast flood levels with better accuracy (as it is linked to 2-d floodplain model); flood risk and hazard maps are direct outputs from such modelling. However, run- time is longer than for the 1-d model. As such, it is not feasible to develop it for all reaches of the river. For selected river reaches, where such modelling will be useful, like in the Lower Terai, this tool shall be developed using dense cross-sectional data (proposed for this study) in combination with DEM. To overcome run-time issues for real time forecasting, GPU (graphical processing unit) or HPC (heavily parallelised computing) versions of modelling software shall be used. In several meetings with DHM, the consultant has proposed the development of a similar FFEWS model, with regards to modelling tools and types of models. We have proposed the same type of advanced 1-d model development for FFEWS, which DHM is presently operating in three different basins (West Rapti, Bagmati and Koshi basin). The same (or similar) modelling software (e.g. MIKE11 and HEC-RAS), for both hydrological and hydrodynamic modelling, has been recommended (in parallel to other software, thus giving DHM wider options to choose from).
Rain gauge network installation Five new auto telemetry rain gauge stations have been proposed for installation. Data will be recorded at 15 minutes interval. There is one existing rain gauge station within this basin, which is not a telemetry station. This will deliver one rain gauge per 85km2 over the basin, similar to England (60km2) where rain gauge density is the highest in Europe. Flood prediction in rural and urban areas requires dense spatial gauge networking, one gauge between 10 and 100km2.
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Hydrometric gauge network installation Four new telemetry hydrometric stations have been proposed. Data will be recorded at 15 minutes interval. There is one existing hydrometric station within this basin, recording water levels, and discharge only occasionally. None of them are telemetry stations. The locations have been chosen carefully to allow for good calibration of runoff from the hydrological model and river levels in the hydrodynamic model. At three stations, both water level and discharge will be recorded, and at one station, only water level will be recorded. Discharge will be measured using ADCP or propeller type current meter depending on flow conditions (at very low flow, ADCP measurement is not suitable). One of the above proposed discharge stations will be a cableway station. Stage discharge rating curve shall be developed at all three discharge stations. The proposed locations will be finalised through discussion with DHM. DHM’s site selection criteria and criteria in other international manuals shall be followed.
Hydrometric equipment ADCP, DGPS and echo-sounder will be purchased for discharge measurement. This set of equipment will be used for discharge measurement and may be shared if available with other basins; each basin will have one set of equipment, as the discharge measurement frequency is fortnightly, and there are three discharge stations within this basin.
Topographic and asset survey The topographic survey will include river sections sufficiently extended across the adjacent floodplain, any existing structures and a flood embankment profile. The survey will have to be done in the Lakhandei river. Along 61km, 161 cross-sections will have to be surveyed. In steep river sections, cross-sections between 200 to 500m intervals are generally essential for accuracy in hydraulic model (HEC-RAS, Users’ Manual, Version 4.1, Figure 8-34). We have proposed cross sections with a 379m average interval.
For the topographic survey, no survey equipment has been proposed for purchase; survey will be done through outsourcing.
FFEWS modelling budget The FF model includes development of models, development of tool for automation of FF operation and development of tool for automation of forecast dissemination. This budget (Table 1) will be required over a three-years period; forecasting will start with the simplest tool from month 8 or 9 of the project using the gauge-to-gauge correlation method. Over the three-years period, advanced sophisticated 1-d, 2-d and 1-d/2-d linked models will be delivered and will remain operation for the years to come.
Table 1: Lakhandei basin FFEWS model development budget Categories Parameter Unit Qty Development Annual Annual Unit cost ($) cost ($): cost: cost Operation Dissemina ($) tion ($) Data: Per basin No. 1 32,500 - - 32,500 collection, processing, analysis Hydrological Catchment km2 425 80,500 - - 189 modelling area Gauge to River km 49 52,000 13,575 11,700 1,061 gauge
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Categories Parameter Unit Qty Development Annual Annual Unit cost ($) cost ($): cost: cost Operation Dissemina ($) tion ($) correlation length Pure 2-d River km 0 0 0 0 0 modelling length 1-d River km 49 68,000 24,000 13,875 1,388 modelling length 1-d/2-d River km 11 80,500 24,000 11,175 7,318 linked length modelling Modelling Suite No. 1 13,000 - - 13,000 software Total 326,500 61,575 36,750 Note: Modelling software license cost is distributed over five basins; software will have multi user network license, and cost shown here is per basin. West Rapti is exclude from software cost Source: Mott MacDonald
The hydro-meteorological data networking budget includes establishing auto-rain gauges (Table 2), and auto and manual water level gauges and discharge measurements (Table 3). Discharge measurement is to be carried over a period of three years, while rainfall and water levels are to be collected for three years for this project period and also to be maintained beyond the period of this project. Measurement of discharge beyond three years (this project period) will be left to DHM’s choice whether further occasional discharge measurement to be carried out (or not) by their trained technical staff (will be trained in this project).
Table 2: Budget for proposed rain gauge networking in Lakhandei basin Meteorological data network Lakhandei budget (US$) No. of No. of Capital cost/ Unit cost Maintenance Total cost station measurement measurement cost: 3 years cost Ground based 5 - 25,000 5,000 6,000 31,000 tipping bucket auto telemetry Total 5 25,000 5,000 6,000 31,000 Source: Mott MacDonald
Table 3: Water level and discharge gauge networking budget in Lakhandei basin Hydrometric data network Lakhandei budget (US$) No. of No. of Capital cost/ Unit cost Maintenan Total cost station measurement measurement ce cost: 3 cost years Discharge 3 90 390,000 4,333 6,000 396,000 Water level 1 - 7,000 7,000 6,000 13,000 Note: a) Discharge measurement to be carried out fortnightly from mid-May to mid-October; this will be 10 measurements per year, 30 in 3 years at one station and total 90 measurements in 3 stations; b) Operation and maintenance cost is $2,000 per basin for all stations per year; this involves routine site visits, repair and maintenance of the gauge, sediment removal etc., c) Discharge measurement cost is a continuous expenditure, like model development cost (and should be considered similar to capital cost); it includes cost for all skilled human resources and the logistics required Source: Mott MacDonald
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Topographic survey will be outsourced, and thus no procurement of survey equipment is proposed; the budget included (Table 4) here is for surveying cross-sections in the Lakhandei river and its tributaries. Budget for purchasing high resolution satellite imageries has been included (Table 5) for Lakhandei basin for lower catchment only in Terai to provide DEM to 1-d and 2-d model development and flood inundation map preparation.
Table 4: Topographic survey budget for Lakhandei basin Topographic cross- Lakhandei survey budget (US$) section survey Length of survey (km) No. of XS Total cost Unit cost Lakhandei 46 121 24,200 200 Tributary 1 (right bank) 7.5 20 4,000 200 Tributary 2 (right bank) 7.5 20 4,000 200 Total 61 161 32,200 - Source: Mott MacDonald
Table 5: Satellite imagery purchase budget for Lakhandei basin High resolution (50cm) satellite imagery 2 area (km ) Total cost Unit cost (USD for one sq.km) Lakhandei basin in Terai 316 15,800 50 Total 316 15,800 - Source: Mott MacDonald
Hydrometric equipment (DGPS, Echo-sounder and ADCP) is a capital expenditure. This equipment set will be used for fortnightly discharge measurement. It will be used and will remain available for discharge measurement over a period of three years and beyond through maintenance of the equipment set. The equipment set also includes cost for construction of one cableway discharge measurement station on the Lakhandei river (Table 6).
Table 6: Discharge measurement equipment / station budget in Lakhandei basin Hydrometric equipment and Lakhandei budget (US$) installation Capital cost Operation and Total cost maintenance (total for 3 years) DGPS 25,000 1,250 26,250 Echo-sounder 25,000 1,000 26,000 ADCP 35,000 1,000 36,000 Cable way discharge station: construction 95,000 - 95,000 cost Total 180,000 3,250 183,250 Source: Mott MacDonald
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1 Introduction
1.1 Project background Acknowledging the importance of the Terai region to Nepal, the Government of Nepal (GoN), through the Ministry of Energy, Water Resources and Irrigation (MoEWRI), is implementing the ‘Priority River Basins Flood Risk Management Project’ in the Southern Terai region. The project is the continuation of the pre-feasibility study, Package 3- Flood Hazard Mapping and Risk Management Project (FHMRMP, 2016).
During the pre-feasibility study from the 25 basins, six priority basins were selected and included in the cost-benefit analysis: i) West Rapti, ii) Mawa –Ratuwa, iii) Lakhandei, iv) Mohana -Khutiya, v) East Rapti, vi) Bakraha. Bakraha replaced the Biring basin; the Khutiya basin was added to the Mohana basin and Mawa was added to the Ratuwa basin.
In this study, feasibility level design for developing a Flood Forecasting and Early Warning System (FFEWS) in the above basins (excluding West Rapti) has been prepared. The FFEWS for West Rapti basin is currently being developed by the Department of Hydrology and Meteorology in Nepal, funded by the Wold Bank in the project ‘Building Resilience to Climate Related Hazards (BRCH)’.
This feasibility report is for the development of FFEWS in the Lakhandei basin.
1.2 Problem statement Nepal is considered to be one of the most disaster-prone countries in the world. Alongside other natural hazards, such as earthquakes and landslides, flooding poses risk to large sections of the population. Heavy damage to infrastructure, loss of agricultural production, disruption of livelihoods and loss of lives in Nepal due to floods are frequent occurrences during summer monsoons. It is also expected that economic losses associated with floods are likely to rise with increasing economic and development activities in the flood plains. Holistic management of flood risk requires actions to reduce impact before, during and after extreme events and includes preventive technical measures as well as socioeconomic aspects to reduce vulnerability to hazards. Although flood disaster risk assessment and management processes have been used by the Government agencies in Nepal to help estimate and manage risks associated with floods, these tools are in general not available (other than for isolated flood and erosion control structures) in the five basins of this study and as a result may not serve these basins in an optimal way. One of the first steps in flood disaster risk reduction is to identify risks. Knowledge of risks raises awareness and allows pre-event planning in contrast to post-event response and recovery. In this context, flood risk management must be coordinated with other development activities in the flood plains, and particularly water resources development in a river basin. To do this, it is necessary to better understand the extent to which the current level of information related to flood disaster risk is adequate for development planning, and societal risk management practice, and whether or not this level can be improved. Besides, it requires assessing the degree to which flood risk management has been integrated in other development activities so far and whether this integration can be improved by a thorough understanding of flood hazards in river basins, especially in Terai with large flat flood plains.
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1.3 Understanding needs of FFEWS One of the key non-structural measures of reducing flood disaster risks is the provision of a reliable and accurate flood forecasting system including a thorough understanding of flood propagation in rivers and inundation of large flood plains. Provision of a reliable and accurate flood forecasting system with adequate lead time has been recognised by the Government of Nepal as a key non-structural measure to reduce flood disaster risks. However, due to lack of an integrated hydro-met monitoring network in these six basins and due to lack of real time forecasting technology and tools, the capability to meet the demands of a modern real-time flood forecasting and warning system is limited. An effective flood forecasting and warning system has to be based on hydrological and hydrodynamic models to simulate rainfall-runoff from precipitation and to simulate propagation of floods along the tributaries, mainstreams and the flood plains. Using real time rainfall data from upper and lower catchments, meteorological forecasts and river gauge data, flood forecasts for up to three days in advance can be developed using the modelling tools. The generated forecasts on flood level and discharge shall be translated easily into understandable warnings including flood inundation maps/risk maps for community-based disaster risk management activities. The forecasts, warnings and risk information should be disseminated as widely as possible via Internet, mobile phones, public and private media, social media and other means of communication.
Flood risk consists of three key components: i) problem of repeated occurrence, ii) exposure of people and assets to flood, and iii) vulnerability. FFEWS will reduce exposure and vulnerability of those exposed.
1.4 Study area The catchment of the Lakhandei river basin lies between Northing 2963052m and 3007157m (26°46'48.80"N and 27°10'54.13"N), and between Easting 344340m and 378230m (longitude 87°32'24.27E and 87°41'24.06 E) in WGS 84, UTM Zone 45 N (see Figure 1). The basin extends from Chure Hills (Siwalik Hills, also known as sub-Himalayan hills, at low altitude) in the north and in Terai (meaning low flat land) up to the Indo-Nepal border in the south. Lakhandei is the main water body within the basin; the key tributary is Chapani Khola in the west. The catchment covers an area of 425km2 in the east of Nepal (Figure 1). The Lakhandei river system lies in the district of Sarlahi in Province no. 2. The basin has 264 settlements distributed over rural and urban municipalities with a population of about 260,000 in 45,000 households (CBS, 2011). Lalbandi, Harwan, Nawalpur, Koudena, Khutauna, and Bhadsar are the major towns and villages located in this basin.
The basin has one hydrometric station, which collects water level and possibly occasionally discharges, and has one meteorological station which collects rainfall.
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Figure 1: Lakhandei basin location map
Source: Mott MacDonald
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1.5 River system in the basin A brief summary of the river system in this basin is presented below in Table 7. Reach-wise detailed classifications of these morphologically active rivers have been presented in a separate report in this study (Mott MacDonald, July 2018). A brief description, mainly useful for developing hydrological, hydraulic and flood forecasting models, is presented below.
This catchment originating from Chure Hills is relatively small (425km2), considerably smaller than the size of West Rapti at Bagasoti gauging station. The West Rapti at Bagasoti has a time of concentration of 10hrs (DHM, 2018). Thus, this basin is expected to have much smaller time of concentration. And this will affect both forecast lead time and response time. In steep slope reaches (upper reaches), the flood travel time, in general, is fast. Therefore, gauge to gauge correlation forecasting, which is practised in many basins by DHM in Nepal, will not be generally suitable, particularly in the upper reach, as both forecast lead time and response time will become small in this basin. Rivers in Nepal are flashy; as such the lag time (time required to attain peak flow after a rainfall event) is very short. This necessitates that rainfall-runoff and hydraulic models are developed for the well-defined reaches of rivers and get the benefit of 1 to 3 days of lead time on rainfall forecast from the weather forecast model. The hydraulic model, irrespective of flood lag time, response time and slope of the rivers, will be able to forecast water level and their propagation with same lead time (1 to 3 days) as in weather forecast model. However, depending on the river characteristics, appropriate type of hydraulic model should be developed. In steep slope reaches, flood propagates fast and flooding spreads less in the limited floodplain; so developing 1-d model will be more appropriate in those reaches. In gentle slope reaches, the flood propagation is slow and flood inundates more areas in meandering and braided floodplain; thus, 1d/2d linked model will be more accurate and beneficial to warn people. Considering the characteristic features of the river systems, e.g. hills, river braiding, meandering, the types and domains of models have been identified (see Section 8). However, development of appropriate type of model (1d, 2d or 1d/2d linked) should require several iterations during development stage of these models.
Table 7: Summary of river system in Lakhandei basin River Reach ID Reach Characteristics Channel Slope (%) length (km) Lakhandei River 1 Hill 22 1.71 2 Fan 14 0.5 3 Peripheral fan 13.8 0.23 4 Flood plain, partially 9.6 0.11 meander/wandering 5 Flood plain, partially meander 11.1 0.09 Source: Mott MacDonald
The Lakhandei river has been divided into five reaches (Table 7 and Figure 2). The reaches are mainly sinuous in hilly areas and straight or transitional sinuous, that is between straight and meandering, reach in Terai. The sinuosity index for all five reaches is between 1.1 and 1.42. Although based on the sinuosity index the river is characterised as straight, the high width/depth ratio (>40) indicates presence of braided channels (multiple channel within the bank), and transverse bars (Park, 1977). Channel slope is gentle (1.71%) in the upper (hill) catchment as compared to other basins studied in this project, i.e. Bakraha, Mawa-Ratuwa and Mohana- Khutiya river basins
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The slope is very gentle (0.5 to 0.09%) for the remaining 48km of the river length. In the first two reaches sediments are mainly boulders, gravels and sands, while in the last three downstream reaches, sediments are sand and silt (and might contain a minor fraction of gravel).
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Figure 2: River system in Lakhandei basin
Source: Mott MacDonald
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1.6 CDMA and GSM coverage in Nepal Real time data acquisition and the issuing and dissemination of flood alerts and warnings from the FFEWS model are the prime objectives. The proposed FFEWS will primarily use the code- division multiple access (CDMA) and Global System for Mobile communication (GSM) technologies of Nepal for dissemination of the flood alerts and warnings. CDMA technology has been used by NTC while the GSM technology is supported by other mobile providers in Nepal. Ncell is one of the companies with the largest GSM networks in Nepal. Both companies are providing services to DHM for real-time data acquisition. However, these service providers have several gaps in their network coverage. In these gap areas, the hydro-meteorological stations cannot transmit data on a real-time basis. Since these technologies are based on the line of- sight communication, some of the hydrometric stations located in deep gorges do not have connection even within the area of their coverage.
The existing CDMA and GSM in Nepal is shown in Figure 3. The Lakhandei basin appears to have good GSM network, and thus will be very useful for establishing the FFEWS in this basin. However, for installation of any new proposed hydro-meteorological monitoring station, GSM must be checked prior to installation, and if needed, station location could be shifted through discussion with DHM.
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Figure 3: GSM coverage in Nepal
Source: DHM, 2018
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2 Hydro meteorological data
2.1 Introduction Certain types of hydro-meteorological data are essential for different types of flood forecast modelling. Meteorological data types are: rainfall/precipitation (P), temperature (T) and potential evapotranspiration (PET); they are input data for rainfall-runoff (RR) modelling in any hydrological modelling tool. Hydrological data types are: discharge (Q) and water level (H); they are required at boundary conditions as well as at calibration and validation locations of rainfall runoff (Q required) and 1-d and 2-d hydrodynamic models (both Q and H required).
Development of the base model, whether for flood forecasting or for design of flood protection works, would not essentially be very different. In order to run more efficiently, the forecasting model could be simplified in some places, though not at the expense of accuracy. The model has to be calibrated against several past events and the calibration process would be enhanced by testing a greater number of events. The model should be able to replicate any event, for a wide range of return periods (from very low to high exceedance probability). Both long-term data and short-term past storm event records can be used for calibration. Long-term data are more appropriate and do not need to be continuous. During the operational phase of the flood forecasting model, collecting continuous data during the monsoon season will be essential as the model will operate daily in real time.
The following data will be required at daily or sub-daily temporal resolution in each phase of FFEWS model development, namely, calibration, validation and operational phases.
● Cumulative rainfall; runoff is the response from total rainfall, rather than a rate of rainfall. As such, cumulative rainfall is required as input to the runoff model ● Mean temperature ● Cumulative PET; for the same reason as rainfall, cumulative PET data is used as input in the runoff model ● Water level ● Discharge Snow cover data may be not be required as the altitude of the basin is below 3000m, and thus it is understood that catchment runoff is not snow-fed (Putkonen, 2004).
For example, in the calibration phase, parameters’ values within the RR models for each of the sub-catchments will be tuned so that differences (errors) between modelled Q and observed Q are minimum and down to acceptable levels. The level of acceptability needs to be agreed with the client (DHM) and with reference to best practice of RR modelling (DHI, 2014; HEC-HMS). During the validation phase, the performance of the model is evaluated, without changing any parameters established during the calibration phase, and a similar standard of matching between observed and modelled Q and WL should be obtained; otherwise, a recalibration would be needed followed by validation.
In the event that observed meteorological data is insufficient or not available in all sub-basins, data from other sources should be explored. This necessitates the consideration of satellite- based rainfall, temperature, snow cover etc. Such data are available as gridded data and are generally available in good spatial resolution which are mainly derived from long records of observed gauge-interpolated data (see Section 2.6 for details).
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In this section, available data from DHM, and gridded data from a number of sources have been discussed.
2.2 Hydro-meteorological gauge densities in Nepal and other countries The density of hydro-meteorological stations in Nepal and in some other countries in the world is presented in Table 8. The density is mainly dependent on purpose, e.g. the rainfall data will be used for irrigation, flood risk assessment or flood forecasting purposes. Flood prediction in rural and urban areas requires dense spatial gauge networking, one gauge between 10 and 100km2 and higher temporal measurement frequency between minutes and hours (Berndtsson and Niemczynowicz, 1988).
Table 8: Rainfall gauge density in Nepal and in some selected countries in the world Country Number Average area per gauge of gauges (km2) Nepal - 550 Nepal: Siwalik region - 430 Nepal: Terai region - 370 UK 3,214 76 England 2,169 60 France - 116 Netherlands - 130 Germany - 88 USA - 1,040 India - 790
Source: DoWRI (2016) for Nepal and Allot (2010), Mett Office, England for other countries
2.3 Existing hydro-meteorological network Nepal DHM is the designated government agency for predicting and disseminating weather-based forecasts and warnings. In June 2018, DHM maintained a total of 175 hydrometric stations, 337 rain gauge stations, 68 climatological stations and 15 synoptic stations. These stations include both real-time telemetry stations and non-telemetry stations.
Among the above stations, DHM presently maintains a network of 28 hydrological stations and 88 meteorological stations as real time telemetry stations. DHM is further upgrading 59 hydrometric stations to real time telemetry stations. An additional seven stations are also under consideration for upgrading to real-time telemetry. In total, 182 hydro-meteorological stations are scheduled to become operational as real-time telemetry data acquisition systems in the near future.
2.3.1 Rainfall Existing metrological stations within Lakhandei basin are shown in Section 5, Figure 6. There is one station within the basin and three more stations within a 4km buffer zone of the boundary of this basin. None of these stations are real-time telemetric stations. These rainfall stations are understood to be daily data. While they will be useful for calibration and validation of the hydrological model, real time telemetry rainfall at higher frequency than daily, usually 15 minutes frequency, will be required for flood forecasting and early warning system. As such, new telemetry rain gauges have been proposed (see Section 5).
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DHM has planned installation of three radar rain gauges across Nepal. One is under installation at Surkhet near Mohana and one at West Rapti; installation will be completed in a few months. This C-band long range radar has a 400km diameter range. Temporal resolution from bid document is understood to be of a minimum of 1 hour; however, the temporal resolution is to be agreed with DHM. The spatial resolution, though not mentioned in the bid document, could be of 1km as obtained by similar long-range C-band radar in UK and Germany (Lengfeld et al., undated).
Therefore, the forecasting model development will initially use existing rainfall radar data from DHM (if available) supplemented with gridded rainfall data available from satellite-based sources (see Section 2.6).
2.3.2 Evaporation The same meteorological stations, which monitor rainfall, also collect daily PAN evaporation data. However, hydrological modelling software, like NAM, uses monthly PET. There is only one evaporation station within this basin and three more stations within the 4km buffer zone around this basin. Pan evaporation data from these stations will be used in the development of the hydrological model within the FFEWS.
2.3.3 Temperature The same meteorological stations, which monitor rainfall, also collect temperature, daily minimum and daily maximum data are available from DHM. There is only one temperature station within this basin and there are three more stations within the 4km buffer zone around the basin. Temperature data from these stations will be used for development of the hydrological model with the FFEWS, in case there is any snow-fed runoff in the basin.
2.4 Water level stations The existing hydrometric stations (water level and discharge) within the Lakhandei basin are shown in Section 6.3 in Figure 8. There is only one water level station within this basin. It is not a real-time telemetric station. Therefore, for the development of hydrological and hydrodynamic models within the FFEWS for this basin, the water levels must be obtained from new proposed monitoring stations, which will be real time telemetric data (see Section 6).
2.5 Discharge stations The existing discharge stations within Lakhandei basin are shown in Section 6.3 in Figure 8. There is only one water level station within this basin. It is not a real time telemetric station. Therefore, for development of hydrological and hydrodynamic models within the FFEWS for this basin, discharge must be obtained from new proposed monitoring stations, which will be real time telemetric. Through conversion of water level to discharge, continuous discharge real time series will be generated by using a discharge vs water level rating curve. The discharge vs water level rating curve will be developed in the project (see Section 6).
2.6 Gridded Meteorological data Gridded time series of meteorological data (rainfall/precipitation, surface temperature, evaporation and snow cover), spreading over the Nepal and border basins in China and India are available from a number of sources. Data are satellite-based, re-analysis based or gauge- interpolated estimates. Gridded time series data shall be needed due to non-availability or scarcity of hydro-meteorological observations within these six priority basins. The gridded products available are:
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● TRMMv7 precipitation estimates ● APHRODITE precipitation and temperature products ● MODIS snow cover products Availability and quality of some of the gridded data are briefly discussed below.
During the model development phase, time series of data could be used from the above sources. However, before use, availability and quality for long records shall be examined.
2.6.1 APHRODITE precipitation data Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) collects and analyses rain gauge observations from thousands of Asian stations; it has about 57 years of daily precipitation (P) dataset available between 1951 and 2007. APHRODITE (APH) precipitation data is gauge-interpolated and takes account of the orographic effect. Temporal resolution of the data is daily; spatial resolution is 0.25° lat/long (approximately 9km cell). APH data also has air temperature with the same resolution as for precipitation data.
APH daily precipitation data is freely available for non-commercial purposes (for Academic Institutions and Researches) provided proper acknowledgement is given. No commercial use is allowed. Use of APH data by the Government of Nepal may be considered non-commercial; however, for use of the APH data, permission has to be granted. The spatial coverage of APH monsoon dataset extends from 60°E longitude in the west to 150°E longitude in the east and from 15°S latitude in the south to 55°N in the north. This means APH data covers almost all of Asia including Nepal. It is available for the period 1951-2007 i.e. 57 years of daily precipitation data for each grid. This implies availability of daily data for calibration and validation of runoff model with long historic records. APH data is available in two spatial resolutions –viz. 0.5° lat/long and 0.25° lat/long. Compared to observed data from existing DHM stations, APH-P provides better spatial resolution of precipitation distribution (as well as for longer periods) over Nepal in this basin and also in the other basins of this study. It is expected that the mean areal precipitation series for each sub-catchment could be obtained from APH-P and will be used for hydrological modelling.
2.6.2 TRMM3B42 Precipitation Tropical Rainfall Measuring Mission (TRMM) is a joint venture between National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA). TRMM3B42v7 is gauge-adjusted version of satellite-based precipitation estimates of TRMM satellites. The products are continuously released with a two months latency period. Spatial resolution is 0.25° lat/long and temporal resolution is three hours. Data are available from January 1998 to the present time and are freely available for all purposes. These data will complement APHRODITE data which end in 2007. Thus, any calibration and validation of hydrological models beyond 2007 will be carried out using these precipitation data.
2.6.3 MODIS Snow Cover Data The data on snow cover is required to decide upon the portion of a sub-catchment where snow melt generated runoff is the dominant process. None of the six basins within this study is considered to be under snow cover for significant durations. NASA’s MODIS snow cover product can be obtained freely from NASA’s MODIS link: https://modis.gsfc.nasa.gov/data/dataprod/mod10.php.
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2.7 Forecasted Meteorological data For operation of FFEWS, Quantitative Precipitation Forecast (QPF) estimates are essential, and Quantitative Temperature Forecast (QTF) estimates are desirable. For PET, flood forecasting models often use average of past monthly records.
The Numerical Weather Prediction (NWP) system of Indian Meteorological Department (IMD) provides QPF and QTF estimates, which could be obtained through FTP access. IMD forecasts have 9km spatial resolution (0.081° lat/long), has three-hours temporal resolution and 72 hours lead time. IMD-QPF is available at a finer spatial resolution than GFS (details below on GFS) for catchments in Nepal.
Generally, the accuracy of the forecasts from any NWP model deteriorates as lead time increases.
IMD-QTF products have the same spatial and temporal resolutions as that of IMD-QPF products i.e. 0.081° lat/long, three-hours temporal resolution and 72 hours lead time.
The Global Forecast System (GFS) is a weather forecast model produced by the National Center for Environmental Prediction (NCEP) of NOAA. The GFS-QPF with 0.25° lat/long spatial resolution, three-hours temporal resolution and lead time of 10 days is freely available. It is understood that DHM already uses GFS and the Weather Research and Forecasting (WRF) model to obtain QPF for forecasting in their existing FFEWS models.
As IMD-QPF has finer spatial and temporal resolution, and GFS-QPF is already in use by DHM, IMD and GFS are considered as the best reliable source of QPF available to Nepal catchments at present. The best approach may be to treat IMD-QPF as the primary QPF should DHM be able to sign a treaty with IMD to obtain the IMD-QPF; otherwise GFS-QPF shall be used as this is already being used by DHM.
2.8 Summary of availability of data A summary of the availability of existing hydro meteorological data and forecasted rainfall data and their sources is presented in Table 9.
DHM has one rain gauge station and one hydrometric station in this basin. The hydrometric station data will not be useful for any component of the FFEWS models as the station is in the Siwalik Hills (not on the main course of the river).
Data from manual stations have been published by DHM in Data Books up to 2016, which indicates that DHM needs about 2 to 3 years to organise, analyse and quality check data before officially publishing.
Satellite based rainfall available for many years; both APHRODITE and TRMM data could be used for running hydrological models.
Table 9: Hydro meteorological data – summary of availability Data type Data Station Collection Collection Availability Latency/ Period source method frequency Publication available Rainfall & DHM Pattharkot Manual Daily Data Book 2-3 years 1953-2016 PAN (East) evaporation Temperature ------Water level ------and
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Data type Data Station Collection Collection Availability Latency/ Period source method frequency Publication available discharge Gridded APHROD - satellite-based Daily Online - 1951-2007 rainfall ITE TRMM satellite-based 3-hourly Online 2 months 1988-present Forecasted IMD satellite-based 3-hourly Online (Near) Real - rainfall time NCEP, satellite-based 3-hourly Online 7 hours - NOAA Forecasted IMD satellite-based 3-hourly (Near) Real - Temperature time Source: Mott MacDonald
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3 DHM and existing flood forecasting models
3.1 DHM’s mandate The Department of Hydrology and Meteorology (DHM) is the sole organization in Nepal responsible for flood forecasting. One of the mandates of DHM is to provide early flood warnings to vulnerable communities and to major stakeholders. DHM, so far, have been able to establish flood Early Warning Systems (EWSs) in major rivers, in a few flashy rivers and in areas downstream of two glacial lakes that are considered potentially dangerous (DHM, 2018). The major river basins covered under EWS are: Karnali, Babai, West Rapti, Narayani, Bagmati, Kamala, Koshi, Kankai and Biring. Details of some of the EWS are discussed in the next Section (Section 3.2). DHM’s aim is to extend flood forecasting services throughout the country.
The re-organized structure of the Government of Nepal implemented on 23 February 2018 has created the Ministry of Energy, Water Resources and Irrigation (MoEWRI). DHM used to operate under the Ministry of Environment and Population but now has been brought under the wing of MoEWRI with a mandate to provide weather and flood forecasts. With a new regulatory setup in place, DHM has been mandated to develop EWS including information dissemination components.
The organizational structure of DHM that combines hydrology and meteorology, puts DHM in an ideal position to generate flood forecasts and issue forecasts and warnings. DHM has also been working with the Ministry of Home Affairs (MoHA), an organisation involved in the entire disaster management cycle, on disseminating flood warnings through National Emergency operation Centers (NEOCs) and District Emergency Operation Centers (DEOCs). DHM has been supported by WMO since its establishment through collaboration on different meteorological activities and activities related to operational hydrology. As a member of WMO, Nepal has access to global and regional meteorological data required for monitoring and forecasting floods. Furthermore, DHM has received funding for several projects from WMO in the past, including projects on upgrading meteorological observation systems, weather forecasting, agriculture meteorology and hydrological services. In collaboration with the International Center for Integrated Mountain Development (ICIMOD) and countries in the Hindu Kush-Himalayan (HKH) region, WMO has been promoting the World Hydrological Cycle Observation System (WHYCOS) under the name of HKH-HYCOS. DHM has been contributing to this program by sharing real-time data for effective flow forecasting for the rivers originating from the Hindu Kush-Himalayan region.
With the implementation of the project ‘Building Resilience to Climate Related Hazards’ (BRCH), the World Bank has supported DHM by upgrading the existing flood forecasting system in Koshi and Rapti. This five-year project started in 2013 and its goal is to upgrade the real-time data acquisition system and establish an end-to-end flood forecasting system. Upgrading of Koshi and Rapti FFEWS are under progress at the moment. Nepal was also able to receive small grants from the Danish and Finnish governments for promoting DHM’s flood forecasting capabilities. Besides collaborating with the international community, Nepal has also been working closely with its neighbouring countries on upgrading its hydrological and meteorological monitoring systems. Since all the rivers of Nepal merge into the Ganga-Brahmaputra river system, Nepal has bilateral arrangements with India and Bangladesh that support the sharing of hydro-meteorological data and flood information.
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Several INGOs are involved in disaster mitigation in Nepal. Nepal Red Cross Society (NRCS) and UNDP are involved in most of the disaster mitigation activities in Nepal. UNDP has been working with DHM to strengthen Nepal’s hydrological services and promoting community-based flood warning systems. Similarly, small-scale community-based flood warning systems have been implemented by other INGOs either in collaboration with DHM or in collaboration with other government agencies and NGOs. Since flood forecasts and warnings are widely used by communities and with several organisations simultaneously involved in disaster management, there are innumerable stakeholders involved (DHM, 2018).
3.2 Existing flood forecasting models in Nepal – an overview Probabilistic Flood Forecasting Model The model was developed jointly through a research partnership with Lancaster University (UK) and the International NGO Practical Action. The model assimilates rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The model predicts future water level at a site where thresholds for warning and danger levels are known. In this approach each gauged site where warning thresholds are defined requires its own model. The model was piloted in 2002 for the East Rapti River. The pilot model was enhanced and extended, expanding over the next 10 years to cover eight river basins across Nepal (Karnali, West Rapti, Babai, East Rapti, Narayani, Bagmati, Kankai and Koshi basins (Gautam and Phaiju, 2013). This model was made operational by DHM. Among them, West Rapti, Koshi and Babai FFEWS have been (or being) upgraded to advance hydrological and hydraulic models (see below).
Advance hydrological and 1-d hydraulic forecast model Nepal currently has operational flood forecasting models for Koshi, West Rapti, Bagmati, Karnali and Babai catchments; based on NAM/MIKE11 or, HEC-HMS/HEC-RAS software which use rainfall QPF estimates from GFS and WRF. These models are rainfall-runoff and hydrodynamic models, which forecast flood flows and flood levels. In addition, Nepal has gauge-to-gauge correlation forecasting covering most of the country, except basins smaller than about 300 to 400km2. This was informed by DHM Forecast Specialist during meetings held with them between July and October 2018.
The Koshi flood forecasting model uses NAM modelling software for hydrological/rainfall runoff simulation, and MIKE11 for advanced (fully dynamic) 1-d river flow modelling. As the model uses rainfall forecasts from the GFS and WRF model, the lead time is up to 72 hours. The West Rapti flood forecasting model is under development, in the same NAM/MIKE11 modelling system and thus, has a similar forecasting ability as the Koshi flood forecasting model.
The Bagmati forecasting model is also operational and is developed in the HEC-HMS and HEC- RAS modelling system.
3.3 Examples of operational flood forecasting models from other countries Bangladesh has a dedicated flood warning centre in existence for 30 years, which has a FFEWS for the entire country’s river system in one model; the model is referred to as a super model, developed in the advanced hydrological (rainfall-runoff) modelling tool NAM and 1-d hydrodynamic model developed in the modelling tool MIKE11. The Bangladesh FFEW model has a 72-hour lead time. Similarly, advanced FFEW models have been developed in India for the Bagmati river basin in Bihar, the Krishna and Bhima river basins in Maharashtra, and the Brahmaputra river basin in Assam. Unlike those of Bangladesh and India, the UK’s FFEW models are for smaller catchments/basins, because of the hilly terrain of the country and require
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separate individual models for each basin. Nepal will also need to develop similaar separate models for individual priority basins. The UK’s FFEW models are developed in PDM (runoff modelling software) and Flood Modeller Pro (hydrodynamic modelling software).
3.4 Dissemination of forecasts DHM disseminates flood forecasts and early warnings from national level, from where the flood warning is communicated to the community level. The flow of information is shown in Figure 4. DHM disseminates the forecast to DHM basin offices and to NEOC. NEOC tthen inform Provincial Emergency Operation Centers, who transmits the forecast to District Emergency Operation Centers (DEOC). DEOC then disseminates the forecast to media and through different committees to the local community.
DHM, as mentioned in Section 2.3, has upgraded many rainfall and hydrometricc stations to automatic real time telemetric stations. The automated telemetric gauge network links to a national-level DHM-managed web-based flood early warning system, which moniitors rainfall and river level, with real-time data publicly accessible through the DHM website (Shrrestha et al., 2014). Data is transmitted to the DHM server every 15 minutes, with flood warning bulletins available on www.hydrology.gov.np throughout the monsoon period (Gautam and Phaiju, 2013).
Figure 4: Dissemination of warnings
Source: DHM (Courtesy Mr Binod Parajuli, Hydrologist/Forecaster, Flood Forecasting Section)
Quality management of the forecast is evaluated based on feedback. This includes self- assessment as well as responses from stakeholders and community members. Feedback helps to review FEWS and reassess its reliability, accuracy and usefulness. Benefits of an early warning system depend on the response programs. If warnings are either neglected by the beneficiary community and relevant agencies or fail to reach the affected individuals and entities, then such FEWS is not worth investing iin.
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4 Flood forecasting modelling
4.1 Flood forecasting modelling framework Flood forecasting models vary in complexity from simple gauge to gauge correlations to highly automated integrated catchment flood forecasting models. The degree of automation and sophistication should be considered based on the needs for a particular location and a particular community. While the type of forecast system will typically be based on existing hydrometric networks or the enhancement or development of new networks, the system will need to be achievable and affordable. The type of flood forecasting system will therefore depend on:
● Available data ● Basin characteristics/complexity ● Accuracy and reliability required ● Lead time requirements ● Needs of the flood risk communities ● Ability of the operating organisation to routinely operate, maintain and update the models Catchment aspects that affect the timing and magnitude of floods are wide and varied. These might include:
● Degree of catchment urbanisation ● Presence of reservoirs and flood storage/attenuation ● Quality of the ratings at gauging stations ● Impact of tributary and ungauged catchments ● Impact of backwater effects, confluences and tidal locations ● Seasonality of rainfall ● Upland areas and snowmelt considerations ● Influence of groundwater An operational flood warning and forecasting system typically uses some form of hydrological and hydraulic modelling to provide sufficient lead time to avoid loss of life and to allow flood defence measures to be operated. Forecast models are at the heart of reliable operational flood warning systems.
Lakhandei basin’s runoff is primarily rain-fed. The upstream catchment in the Chure hills, is below 3,000m AMSL and thus, runoff mainly originates from rainfall with a minor component from groundwater (base flow); snow-fed runoff normally should not need to be considered for catchments below 3,000m AMSL (Putkonen, 2004).
In the mathematical modelling system for flood forecasting in Lakhandei basin, three major approaches could to be used:
1. Gauge to gauge correlation: this is one of the simplest forecasting tools, based on correlation between gauged water level at two stations: an upstream base station and a downstream target station. This method uses flood levels at the base station when the flood has actually arrived (i.e. it uses the real time observed flood level) to estimate the future water level at the target stations. The lead time for forecast at the target station is small, a maximum of probably five to six hours in this basin.
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2. Rainfall-runoff/hydrological modelling: Rainfall – runoff process modelling could be used to improve the forecast accuracy and lead time. The runoff in catchments with elevation below 3,000m AMSL can be modelled using a continuous rainfall-runoff model. Since the rivers are perennial in nature, the base flow component in the rainfall-runoff process is adequately represented. These continuous rainfall-runoff models typically require rainfall and PET as input to provide catchment flow as output hydrograph. 3. Channel routing/hydrodynamic modelling: Channel routing is the process which describes the propagation of flood wave along the river. This process can be modelled using hydrologic and/or hydrodynamic modelling. The employment of the model depends upon the need and complexity in the system. 1-d, 2-d and 1-d/2-d linked hydrodynamic models can be developed. In terms of what types of modelling techniques should be used, the following key factors should be considered:
● Purpose of the study ● Level of complexity for both in-bank flows and out-of-bank flow paths ● Flow controls and structures in the river system ● Flood storages and their representation in the model ● Requirements on the level of model accuracy ● Computational resources available ● Data availability and accuracy ● Availability of time and budget It is important to use the most appropriate modelling tool for the project rather than merely the tool that is available. Inappropriate tool selection (such as i) the use of a steady flow model where unsteady flow conditions is prevalent or where storage is important, and ii) the use of a 1- d unsteady model, where 1-d and 2-d linked model is most appropriate, like in a dense urban area) can have significant technical and accuracy implications.
4.2 Objectives of flood forecasting modelling The FFEWS shall deliver the following in each basin:
● Implementation of a web-enabled Windows Application on server for routine operation of FFEWS models, dissemination of forecast and routine update of FFEWS models. ● Integration of the knowledge base from hydrological, hydrodynamic modelling and data analysis. ● Seamless connection to temporal and spatial database. ● Dynamic front end module for modifying model inputs, recalibration, data assimilation and dissemination of model results. ● Processing of flood forecasting results in GIS environment into maps of flood inundation extent, depth, arrival time, and duration, with other relevant themes in the background; this will particularly be necessary in the river reaches of 1-d models. ● In 1-d/2-d linked model reaches, a flood inundation map will be a direct output on the forecasting website and ready for dissemination. ● Design and development of appropriate inundation mapping tools, using appropriate satellite/LiDAR derived from DEM for the critical floodplain to predict inundation. ● Development of a module to generate institutional and community targeted inundation forecasts and alert messages (via SMS) and web enabled maps.
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● Implementation of a standalone user-friendly application, which integrates the flood forecast model with knowledge base of topographic and thematic GIS data and has the capability to run automatically. ● Module for embankment breaching scenarios or structural failure scenarios, in-built in the forecasting tool; magnitude of flooding through breaching can be more devastating than flooding from embankment overtopping. ● Design of appropriate format, content and dissemination protocols to accommodate the current practice within DHM or as an improvement to the DHM practice for the Flood Alert to flood-affected residents, both designated and broad category to mobile phones in the likely- affected areas. ● Post evaluation of performance of forecast accuracy with respect to level and timing each year. ● Obtain feedback from stakeholders and incorporate suggestions on dissemination of forecasts and warning.
4.3 Gauge-to-gauge correlation Flood forecasting in Nepal is mainly operational based on level-to-level (also called gauge-to- gauge) correlation across the country, except in a few basins where advanced hydrodynamic modelling is being developed and applied.
The level-to-level forecasting tools use observed water level data at upstream locations (base stations) to forecast water level at downstream target locations. This method is simple and most cost effective, as it only requires water level data in real time at the base station and at the target station.
Flood forecasting from gauge-to-gauge relationships has the limitation of needing to wait until the flood is observed at the base station upstream of the forecasting stations. Therefore, in the process, the possible lead time from the catchment lag (from rainfall to water level response) to the base station is lost. Such lead time can be easily added by introducing a hydrological model that can transform the observed precipitation into a simulated hydrograph at the base station. In case of combined use of runoff model and gauge-to-gauge correlation, the base station must have a stage-discharge rating curve, so that forecast runoff from the hydrological model could be transformed into forecast water level using the rating curve.
The gauge-to-gauge correlation procedure does not incorporate any addition of flow between the stretches from base station to the forecasting station nor does this approach consider any breach or overtopping of the embankments between the stretches. The method also does not provide any other information on the water surface profile, e.g. cumulative effect of many control structures in the stretch that is very crucial from the embankment safety and out-of-bank flow situation. Another major limitation to the correlation method is the availability of prediction only at selected sites (target stations) and not all along the main river, let alone the tributaries. Flood maps cannot be generated due to the coarseness of the forecast stations as the forecasts will be only at the gauging points along the river which are normally sparse. Thus, in this method, the inundated area likely and the time, depth and duration of inundation, which are essential for effective flood management, are not provided to relevant agencies and communities.
4.4 Hydrological modelling Hydrological, i.e., rainfall-runoff modelling can be carried out by various modelling software. One essential element is that the rainfall-runoff model should have a proven record of coupling with a river model (hydrodynamic), so that runoff can be applied as inflows to the river model. A
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coupled hydrological and hydrodynamic model is essential for designing flood forecasting and warning and will allow much higher lead time in forecasting. Some selected hydrological modelling software are discussed below:
HEC-HMS (coupled to the 1-d river modelling tool HEC-RAS 1d and 2d) NAM (coupled to the 1-d river modelling tool MIKE11 1d and MIKE21FM) PDM (coupled to the 1-d river modelling tool Flood Modeller Pro, previous name ISIS and Infoworks ICM Live)
NAM (DHI, 2016) and HEC-HMS (US Army Corps of Engineers, 2017) are deterministic, lumped, conceptual hydrological models, comprised of a set of linked mathematical statements describing, in a simplified quantitative form, the land phase of the hydrological cycle. They mainly simulate surface and sub-surface runoff, and base flow components. The model parameters require calibration against observed runoff. These parameters remain fixed (constant) over time.
The distributed runoff model, e.g., Probability Distributed Model (PDM) from the Centre of Ecology and Hydrology (CEH Wallingford, 2016) accounts for seasonal variation and spatial and temporal effect on parameters (e.g., soil-moisture deficit).
There are many other hydrological modelling systems (e.g., URBS). However, the coupling of a hydrological model with the hydrodynamic model, the capability of automatic parameter adjustment (auto calibration) and the long-term continuous simulation ability are important when considering a selection of tools.
4.5 Routing modelling Normally, the upper catchment is simulated without any need for inclusion in the hydraulic model. If in the upper hilly catchments in Siwalik hills, the travel flood waves and their volume and attenuation are found to be important, some of the tributaries in the hills could be considered for routing modelling, e.g. by Muskingum-Cunge flood routing units. The topography could be extracted from DEM for such modelling.
4.6 Hydrodynamic modelling Hydrological and hydrodynamic modelling for flood risk/flood inundation are the key components of a FFEWS. Logically, hydrological and hydrodynamic flood inundation models are developed first, and then transformed into flood forecasting models.
Hydrological and hydrodynamic flood inundation models for flood forecasting purpose are calibrated and validated over a wide range of flood events, ranging from low flows to extreme high flows. During a monsoon, an extreme flood event can occur; as well a low magnitude flood can also occur. As such a FFEWS model should be ready and applicable for any flow condition. In a hydrodynamic model, accuracy of results, convergence of results (oscillation free results) and stability of model are three key controls. A model, which is suitable and developed for low flows, may generate instability at high flows due to large depths in the channel and shallow depths in the floodplain. As the flood forecasting models will be run in real-time, they have to be suitable for all flow conditions. The forecast model run must not crash during monsoon period, e.g. due to model instability. Flood inundation models, once calibrated and validated for a wide range of flows, will then help to establish the locations for flood warning and to set -up flood alert and warning threshold trigger levels in rivers for out-of-bank flooding, agricultural land flooding and property flooding.
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Flood forecasting models operate in real time, and thus, model run-time has to be efficient. To make the model run-time fast, the model will be simplified in reaches away from the area of flood forecasting points. This will be done by increasing the distance between computational nodes to the maximum permissible limit and by removing other model units, which are less important, e.g. structures which do not create any significant head loss. However, care should be taken as such that the simplified models maintain the same accuracy at all forecasting and calibration points. This process will require several trials so that the model accuracy is preserved.
4.7 Modelling software Some widely used flood forecasting modelling tools are presented in Table 10. The modelling software presented are mostly popular and widely used; thus, the list is not exhaustive. A more detailed list is presented in Appendix A.
Table 10: Examples of key modelling software for flood risk and flood forecasting modelling Model Type Modelling Tools/ Technology Hydrological/rainfall runoff modelling Any lumped conceptual catchment runoff model, e.g. NAM, HEC-HMS, and PDM Hydrodynamic: flood inundation and flood MIKE11, MIKEFLOOD, MIKEURBAN, MIKE21, MIKE risk modelling GPU MIKE21FM, HEC-RAS, FLOOD modeller Pro (former name ISIS), TUFLOW Classic, TUFLOW FV, TUFLOW GPU, Info-works ICM Flood forecasting and warning NAM and MIKE (11, 21FM, URBAN), and HEC-HMS and HEC-RAS, and PDM and Flood Modeller Pro and TUFLOW HPC/GPU, Infoworks ICM Live etc. Source: Mott MacDonald
Hydrodynamic modelling techniques are at the core of fluvial flood risk assessment and flood forecasting and warning (WMO, 2011). As a common practice which started in previous decades, hydrodynamic models are often developed and used to simulate the flood water in the river system as well as across the floodplain. They are used to predict the flood depth, water level, velocity, flood extent and even flood hazard level. Generally speaking, the river system is represented using 1-d models as the flow travels in the channel direction when it remains in the river channel, whilst the floodplain is represented using 1-d, quasi 2-d or 2-d models as the flood water spreads in different directions when the water exceeds the river banks. The 1-d river channel and the floodplain models are linked to represent the connection between the river and the floodplain.
It is important to use the most appropriate modelling tool for the project rather than merely the tool that is available. Inappropriate tool selection (such as use of a steady flow model where unsteady flow conditions are prevalent and where storage is important, and use of a 1-d unsteady model, where 1-d and 2-d linked models are most appropriate, such as in dense urban areas) can have significant technical and accuracy implications for both current and future needs. Several modelling software tools have been tested through benchmarking studies (EA/Defra, 2004 and 2013) and are being employed for flood risk mapping studies around the world (Table 11). In general, software, which has not been subject to benchmarking, is not recommended for developing models.
A list of benchmarked hydrodynamic modelling software is presented in Table 11. Software on this list are widely used in flood risk assessment and flood forecasting in UK, Australia, USA, Bangladesh, India, Nepal and other countries. The benchmarking research of the software was
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conducted by Environment Agency and Defra of UK (EA/Defra, 2013). DoWRI and DHM will have the option to choose from the list. If required, it is recommended to consult recommendations and results of the benchmarking study and choose the suite of software best suited for Nepal.
Table 11: List of benchmarked modelling software and weblink for the respective developer Software 1D 2D 1D–2D Source MIKE11/21 http://www.dhigroup.com/Software/WaterResources.aspx
HEC-RAS 1-d http://www.hec.usace.army.mil/software (see note) Flood Modeller 1 https://www.floodmodeller.com/about/ (previously ISIS) SOBEK https://www.deltares.nl/en/software/sobek/ InfoWorks ICM http://www.innovyze.com Live
JFLOW 2 http://www.jbaconsulting.co.uk
TUFLOW 3 1,4 http://www.tuflow.com 1Available through ISIS-TUFLOW link; 2Not fully hydrodynamic (does not solve momentum); 3Available as ESTRY (provided with TUFLOW); 4Links to ESTRY
Note: HEC-RAS 2D and 1D-2D linked modelling version of software were released in 2016 Source: English Environment Agency (http://evidence.environment- agency.gov.uk/FCERM/en/FluvialDesignGuide/Chapter7.aspx?pagenum=5
4.8 Modelling software comparative list In addition to the list of benchmarked software, a comparative tabular list of widely used other hydrological and hydraulic modelling software is presented in Appendix A. DoWRI and DHM will have the opportunity to choose a suite of modelling tools from this list. In the list, HEC-RAS is a free software, while most of the other widely used software are licensed software. For 1-d/2-d linked modelling and pure 2-d modelling, some software tools such as MIKE FLOOD, MIKE21 and TUFLOW have an advantage as they have been used for several decades. HEC-RAS 1- d/2-d linked version is a recent release from 2016.
4.9 FFEWS cost consideration A number of cost elements are required to operate a flood forecasting and warning system. The following components may need to be considered as part of a whole life cost appraisal:
● Setting up any new organisational structures, if they do not exist ● Installing, operating and maintaining telemetry hydro-meteorological gauge network hydrometric equipment and radar rainfall network if not available ● Maintaining spatial and temporal databases ● Developing, configuring, running and maintaining (and troubleshooting) forecasting models ● Developing, running and maintaining (and troubleshooting) systems for generating and disseminating flood warnings and flood maps ● Buying computer software and hardware to support the above operations ● Obtaining meteorological forecasts from freely available sources, for example, from GFS, WRF and IMD weather forecast models
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● Staff training (continuous) and running flood exercises ● Raising public awareness of flooding and how to respond to flood warnings
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5 Rain gauge network design
5.1 Introduction Rainfall is a main input for rainfall runoff and river flow and flooding models. These models all have different types of requirements of rainfall input data.
Two types of rainfall measurement methods have been proposed:
Point rainfall measurement from ground-based rain gauges Areal rainfall measurement from satellite or weather radar, this also forecasts rainfall which the model needs to read
5.2 Auto telemetry rain-gauge Automatic telemetered rain gauges are proposed for installation as part of this project. Modem (GSM) is proposed as the telemetry data transfer mechanism.
5.2.1 Description The rain gauge should record rainfall and transmit the data through telemetry to the dedicated servers and hydrological and forecasting experts at DHM and consultants via e-mails at defined time intervals.
Au automated telemetry rain gauge system (Figure 5) should consist of a rain gauge unit, e.g. tipping bucket rain gauge, an in-built data logger, a Modem (GSM) for connecting to internet and transferring data to the server. In addition, it should have the facility to access and download data remotely and preferably be solar powered. The tipping bucket rain gauge is to be mounted on a pipe within a stainless-steel enclosure that houses a data logger, modem and battery. Battery charging is to be done via solar panel. The Modem unit must be loaded with a data enabled SIM card purchased from a phone supplier. The user will need to define the time interval of data transfer. At the programmed interval, the modem will initialise a communication with the logger and transmit the rainfall data to the server as well as via e-mails to the flood forecasting experts at DHM and to the flood forecasting consultants of this project.
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Figure 5: A typical auto telemetry rain gauge station
Source: www.phametechnology.com
5.2.2 Time of observation With a tipping bucket rain gauge, rainfall depth is recorded continuously. Rainnfall data is transferred automatically at 15-minutes intervals or at a frequency agreed with client (DHM).
5.2.3 Operation and measurement The bucket tips when a set amount of precipitation, eg of 0.5mm (resolution of bucket), has been colleccted. A pulse from each tip is sensed by the reed switch and llogged to a data logger. The dual reed switch can also transmit the pulse to a telemetry system.
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5.2.4 Data transmission, storage and archive Data should typically be transferred once or twice per day from the gauge station to a central location (server) within DHM for analysis, storage/archive and for FFEWS modelling. Such transfer of data usually increases during times of heightened flood risk. API (application programming interface) shall be developed for access via internet to all gauging stations and for transferring data to the central server. Data could also be transferred to the regional office server where DHM has such facilities. During the model development phase, the modelling team should also have access to the API via internet to download data. In some countries, like the UK, the public can download rainfall data using these API.
5.3 Radar rain gauge
5.3.1 Description Radar rain gauge will not be implemented in this study; however, write-up here is considered for future reference by DHM.
Measuring rainfall by means of radar is not a new technique. The main advantages are that this provides a better spatially-distributed measurement than that obtained from point rain gauge alone. Furthermore, the radar rainfall are grid-based outputs, which are becoming more widely used by rainfall-runoff models. However, limitations include measurement accuracy, range, attenuation of signal and calibration, which means that radar measurement does not necessarily provide great advantages over ground-based rain gauges. Despite these limitations, radar rainfall is useful data which can supplement ground-based data if missing from a rain gauge, help correcting suspicious ground-based data and still be used for rainfall runoff modelling where suitable. Ground-based rain gauge data will be used to ground truth radar rain gauge. Capital expenditure as well as running costs are high, though there are low-cost short-range radars.
5.3.2 Specification In the mountainous terrain of Nepal, precipitation is highly variable both in space and time because of orographic effects and interactions of mountains with wind fields. Moreover, narrow valleys surrounded by high reliefs cannot be effectively monitored by any of the common long- range weather radars because their beam cannot penetrate deep into the valleys due to the shadow effect.
In mountainous regions, the gauge network needs to be very dense (Volkman et al., 2010). Thus, to supplement long range radars, X-band short range radar is a good alternative to the common long-range C-band radar for observing precipitation within a valley. It can be installed directly inside the valleys, at lower altitude. Rain gauge networks can be complemented by short range X-band radars. They can provide rainfall estimates with high spatial and temporal resolution, and their installation cost is less expensive than C-band radar, allowing the placement of more sensors in order to gain optimised coverage. Examples of such radars are: CASA radar by the Remote Sensing Group (RSG) of Polytechnic of Turin and Local Area Weather Radar (LAWR) by FURUNO, Japan. X-band radar for rainfall estimates should comprise of the following specifications:
● Range of 30km to 70km with radar maps produced at 75m to 150m resolution; ● Fitted with series of anti-clutter filters in order to recover, as far as possible, rain signature even in the presence of clutter;
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● Radar maps produced by the X-band mini radar unit are transmitted to the server via in-built communication network through internet (telemetered); ● Provided with processing software for radar maps of rainfall intensity; ● Un-coherent, pulsed, one polarisation only, non-Doppler, with a fixed elevation of the antenna; exclusively devoted to rain measurements and able to produce one rain map in a few seconds; ● Minimum or maintenance-free and possible to be remotely controlled with software adjustments; routine maintenance will be included in the procurement package during the warranty period; ● All the electronic equipment (antenna, radiofrequency unit, data processing unit, communication unit for data transmission and remote control, power unit) are placed inside a dome; and, ● All software is required to operate in dedicated applications in open source in order to allow greater reliability and flexibility in the configuration and full control of active processes and packages, as well as low costs. Data will be available in a commonly used data format.
5.4 Rain gauge network recommended for installation Five new auto telemetry rain gauge stations have been proposed for installation in the Lakhandei basin under this project. The gauges will be tipping bucket auto-telemetered using GSM. The Lakhandei catchment area is 425km2; this will deliver a rain gauge station density as one rain gauge per 85km2, similar to England where the rain gauge density is the highest in Europe (one rain gauge per 60km2; Allot, 2010).
Gauge network density depends on many factors, particularly spatial and temporal resolution of rainfall over a basin and the purpose of the gauge network (e.g. irrigation management, flood forecast etc.). Flood prediction in rural and urban areas requires a dense spatial gauge network, one gauge between 10 to 100km2 and higher temporal measurement frequency between minutes and hours (Berndtsson and Niemczynowicz, 1988). The gauge density may even be higher, like in mountainous areas of Nepal (Lopez et. al., 2015 and Volkman et al., 2010), considering greater variability of rainfall between the mountains in the Siwalik and Terai regions. With consideration of practicality, management, and with reference to other countries, the implementation of five new stations for this basin is considered to be a practical trade-off between cost and benefit. All proposed rain gauges should be stationed in or near settlements (for better accessibility and maintenance) and have been distributed considering the main channels and their tributaries. Their positions are shown in Table 12 and Figure 6.
Table 12: Proposed new auto telemetric rain gauge stations in Lakhandei basin Catchment/basin Rain gauge ID Name of Station coordinates name nearest Longitude, E Latitude, N settlement (deg) (deg)
Lakhandei L_Rain_01 Ghumne 85.673 27.138
L_Rain_02 Bhoktini 85.606 27.104
L_Rain_03 Karitol 85.556 27.052
L_Rain_04 Baheriyan 85.507 26.954
L_Rain_05 Sakraul 85.493 26.844
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Source: Mott MacDonald
Figure 6: Existing meteorological stations and proposed new auto telemetric ran gauge stations in Lakhandei basin (existing rain gauges within 4km buffer of the basin are also shown)
Source: Mott MacDonald
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5.5 Budgets for proposed rain gauge network installation The budget estimates for the proposed rain gauge network are considered for ground based telemetric stations. The budget includes procurement, installation, testing, calibration, monitoring, and operation and maintenance for 3 years. Budgets are shown in Table 13.
Table 13: Budget for proposed rain gauge networking in Lakhandei basin Lakhandei budget (US$) No. of Capital Unit Maintenance cost: 3 Total cost stations cost cost years ground based tipping 5 25,000 5,000 6,000 31,000 bucket auto-telemetry Source: Mott MacDonald
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6 Hydrometric network design
6.1 Water level gauge network
6.1.1 Description Flood risk assessment and flood forecasting rely heavily on hydrometric data. Key hydrometric data requirements include river levels, river flow and ground water level.
Any of the following water level monitoring methods are proposed for this project; each approach is telemetric monitoring.
● Measurement by float in a stilling well ● Water Level Radar Sensor ● Water Level Bubbler Sensor Data will be automatically transferred to the dataset server by GSM telemetry.
Depending on site conditions of the gauging station, one of the monitoring approaches shall be selected. At each telemetric water level station (whether stilling well, radar or bubbler), there will be a manual water level staff gauge. This manual gauge shall be maintained by CBDRM Committee members and can be used by them in the event of a flood alert to communities. This staff gauge should have different distinct colour painting for water levels in flood alert zone, in flood warning zone and in danger level zone (DHM uses such colour level staff gauge).
The water level stations will be included as a forecast point in the FFEWS.
Specification of telemetry kit
The telemetry gauging station should allow:
● Remote monitoring of river levels; ● Transmission of alarms if level rises above user defined thresholds; ● Viewing of historical level data via simple web GUI; ● Transfer of data via API for use in applications and websites; and, ● Preference for solar powered; which will also include a backup power system (battery). The telemetry system should contain:
● Level Sensor having different options to suit depth of river from shallow depth (0.2 to 0.5m) to several metres of depth (>20m); accuracy 0.5% of range; ● Data logger; ● Solar Powered Telemetry Unit with GSM module and antenna built in; ● Sim Card for the warranty period of 5-years, multi network; ● Readings every 5 minutes; and, ● Transmission of web-based data and alarms by email to designated professionals (DoWRI/DHM to provide list of emails of designated persons and professionals, and thresholds for high water levels).
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6.1.2 Time of observation Stationarity of record, temporal resolution and the overall accuracy of water level data shall be considered. All water level records will be taken at 15-minute intervals in to the data logger, and are to be transmitted via telemetry to the central dataset server at DHM and to the hydrological and database experts at DHM.
6.1.3 Operation, measurement and maintenance Backup and main recorders should be securely mounted and regularly visited and serviced at least once every month (or more if required). It should be ensured that the pulleys are operating freely, and the float tape or wire sits properly on the drive pulley. Logbooks should be maintained, and calibration of the level sensor should be checked and reset if required.
Sites with known sediment problems shall be carefully checked at each visit, and if there are any indications of a siltation problem, the stilling well must be flushed as soon as possible or proper flow connection to the sensor must be maintained. The Lakhandei basin carries highly sediment laden flow, and thus routine silt management will be required
6.1.4 Data transmission, storage and archive Data will typically be transferred once or twice per day to a central location dataset server within DHM for analysis and storage/archive. Such transfer of data usually increases during times of heightened flood risk. In periods of heightened flood risk, even hourly transfer could be required considering the flashy nature of a storm event in the basin. Data could also be transferred from the gauge stations and/or from central dataset server to DHM’s three basin Offices (Karnali Basin Office in Nepalgunj, Narayani Basin Office in Narayanghat and Kosi Basin Office in Biratnagar), and.to the regional offices where DHM has data storing facilities. During the model development phase, the modelling team should also have access to the real time data.
6.2 Discharge measurement station
6.2.1 Description Manual discharge measurement stations should be capable of measuring low to moderate flows while the water remains in the channel. For measuring flow beyond certain thresholds, especially when the water level is very high, exceeding the river banks and flowing across the floodplain, the river flows are normally derived from the relationship of stage (level) with discharge, called a stage-discharge relationship or rating curve.
6.2.2 Discharge measurement equipment Discharge measurement at all stations shall be carried out for a wide range of flows, from low flow to high flows. Discharge measurement is to be carried out fortnightly from mid-May to mid- October at each station. A combination of the following discharge measurement methods can be used:
Manual measurements using current meter (propeller current meter) during low flows except at the cableway station Velocity-depth measurements with ADCP (Acoustic Doppler Current profiler) during medium to high flows, except at the cableway station Cableway discharge measurements using propeller current meter (ADCP may also be mounted if required) at the uppermost hilly station, where velocity measurements at medium to high flows are not advisable without cableway owing to safety reasons
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In manual measurement during low flows, cross-section/depth will be measured using a graduated pole, often referred to as manual sounding. When depth is increasing and ADCP (with echo-sounder and DGPS) could be used, all discharge measurements shall be carried out using ADCP. While ADCP will provide velocity scatter, echo-sounder will provide depth and DGPS will provide horizontal positioning. The measurements will be carried out using local engine boat/inflatable boat. The ADCP discharge measurement boat could be un-manned. The ARC-Boat (http://www.ceehydrosystems.com/products/unmanned-survey-vessels/arc-boat/) is designed to make safe unmanned discharge measurements in rivers and streams using acoustic ADCPs. The hull design minimises air entrainment for optimum ADCP data quality. With a maximum speed of 4.5m/s (15fps), even high velocity flood stage measurements may be completed. Effects of magnetic interference from the vehicle’s electrical systems are carefully managed to minimise induced compass deflection – critical to obtaining good discharge measurements.
ARC-Boat ADCP measurement at the cableway station could also be considered to replace Cableway depending on the magnitude of velocity and safe operation of measurement.
6.2.3 Cableway flow measurement Slack-line cableways are commonly used for carrying out flow gauging on relatively small rivers and streams. This is particularly useful in rivers in mountainous regions with steep slopes and high velocities. Velocities are measured from a velocity traverse set at a series of fixed depths, and then multiplied with cross-section area providing total discharge through the river section. Such discharges are useful for developing stage-discharge rating curves. Cableway stations are not suitable when flow goes out of bank and cross section width is high, e.g. river sections in the Terai region.
The components of a slack-line cableway comprise: a static ropeway, suspended between anchor ends; a traveller; a horizontal positioning mechanism; and a lifting mechanism. In operation, the traveller runs on the ropeway and functions as an unmanned ‘cable car’. All operations are carried out from the bank. The horizontal position of the traveller is controlled by means of a manual winch that feeds a line across the span and over a pulley mounted on the far side and back to the traveller. A separate line from a gauging reel feeds out to the traveller, runs over a pulley mounted on the traveller, and connects to a current meter and counterweight, thereby suspending these from the traveller. The gauging reel controls the vertical position of the current meter. Depth will be taken by the sounding reel cable at pre-defined vertical positions by lowering the cable to the river bed; then depth data will be converted to obtain cross-section (river bed level with respect to masl) using the water level gauge reading. As ADCP, DGPS and echosounder will also be available, these set of equipment could also be occasionally mounted to the cableway for depth and velocity measurement, if the Equipment Engineer find it practical.
DHM operates cableway discharge measurement in West Rapti at Kusum. A similar cableway should be established at a station at Daphkalitol station (or at a suitable nearer station) over Lakhandei river. Standard specification for establishing cableway could be found in manuals, e.g. NEMS Manual (NEMS, 2016) and in USDA, 2000. A schematic of cableway system discharge measurement is shown in Figure 7.
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Figure 7: Schematic of a cableway discharge measurement system with sounding reel and current meter: (1) near post with pulley drive housing, (2) sounding reel, (3) cableway cable, (4) sounding reel cable, (55) traveling block, (6) current metter, and (7) tailhold on far side
Source: USDAA, 2000
6.2.4 Equipment budget for discharge measurement Acoustic Doppler Current Profiler for discharge measurement, supported by ecchosounder (depth measurement) and DGPS (position measurement), have been proposed. The budget for one equipment set only for this basin is presented in Table 14 below. There will be extensive flow measurements at three stations (fortnightly at each station) in this basin. Thus there is need for one set of equipment for this basin alone.
There will be a warranty period of 3 years for DGPS, Echo-sounder and ADCP. However, a budget has been considered for operation, maaintenance and servicing. As the equipment set will be used extensively, such cost is necessary.
Table 14: Discharge measurement equipment / station budget in Lakhandei basin Hydrometric equipment and installation Lakhandei budget (US$) Capital cost Operation and Total cost maintenance (total for 3 years) DGPS 25,000 1,250 26,250 Echo-sounder 25,000 1,000 26,000 ADCP 35,000 1,000 36,000 Cable way discharge station: construction 95,000 - 95,000 cost Total 180,000 3,250 183,250 Source: Mott MacDonald
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6.3 Hydrometric gauge recommended for installation Four hydrometric gauging stations in Lakhandei catchments have been proposed (Table 15 and Figure 8). All stations are easily accessible and located in the neighbourhood of a settlement. All stations have access to GSM (DHM, 2018).
The locations have been chosen carefully, which will allow calibration of runoff from hydrological model and calibration of river levels in the hydrodynamic model. There will be three water level (WL) and discharge (Q) stations (both parameter at same station), and one WL only (no Q) station proposed for the Lakhandei basin.
Stage-discharge rating curves will be developed at all three discharge stations for generating continuous discharge data and for calibration and validation of hydrological and hydrodynamic model.
At all four hydrometric stations, bed material samples shall be collected; and in three discharge stations, suspended sediment samples shall be collected. Bed material shall be collected from mid-channel, and from bed near the banks. Concentration shall be collected from three positions as minimum over the cross-section. If river depth is high (>3m), collecting concentration over several vertical positions (e.g. 0.2d, 0.6d and 0.8d where d is total water depth) at each position is recommended. If the depth is shallow (<3m), only one sample at 0.6d is recommended.
The above Q and WL station distribution will allow calibration and validation of runoff from catchment and hydrodynamic model of the river. The distribution of WL and Q stations will allow a considerable dense network for calibration of the hydrodynamic model; such dense network should be considered essential given the relatively steep slopes in upper basin and mild slopes in lower basin (Mott MacDonald, July 2018).
Table 15: Proposed water level and discharge stations in Lakhandei basin River name Water level Gauge type Name of Station coordinates and nearest Longitude, E Latitude, N discharge settlement (deg) (deg) gauge ID
Lakhandei L_G_01 Water Level Baheriyan 85.505 26.964
L_GD_02 Water Level and Discharge Daphkalitol 85.638 27.087
L_GD_03 Water Level and Discharge Pataura 85.577 27.036
L_GD_04 Water Level and Discharge Shivanagar 85.515 26.823 Source: Mott MacDonald
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Figure 8: Existing water level and discharge station and proposed stations in Lakhandei basin
Source: Mott MacDonald
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6.4 Hydrometric gauging network budget Budget for hydrometric gauge networking is presented in Table 16. The budget includes procurement, installation, testing, measurement, calibration, monitoring, and operation and maintenance for 3 years.
Discharge measurements will be carried out fortnightly from mid-May to mid-October for three years. The measurements shall also include bed material collection at all four proposed hydrometric station and sediment concentration at the three discharge station only ; this will be 10 measurements per year, 30 in 3 years and total 90. Operation and maintenance cost is $2000 per basin for all stations per year; this involves routine site visits, repair and maintenance if required. Sediment measurement will have multiple benefits including supporting working design of river training works and also in FFEWS, for example, improving stage-discharge rating current; changes in sediment load shall indicate need for updating the rating curve.
Table 16: Water level and discharge gauge networking budget in Lakhandei basin Hydro-meteorological data networking Lakhandei budget (US$) No. of station No. of Capital cost / Unit cost Operation & Total meas Measurement Maintenance cost urem cost cost: 3 years (US$) ents Discharge 3 90 390,000 4,333 6,000 396,000 Water level 1 - 7,000 7,000 6,000 13,000 Note: a) Discharge measurement to be carried out fortnightly from mid-May to mid-October; this will be 10 measurements per year, 30 in 3 years at one station and total 90 measurements in 3 stations; b) Operation and maintenance cost is $2,000 per basin for all stations per year; this involves routine site visits, repair and maintenance of the gauge, sediment removal etc., c) Discharge measurement cost is a continuous expenditure, like model development cost (and should be considered similar to capital cost); it includes cost for all skilled human resources and the logistics required Source: Mott MacDonald
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7 Topographic and asset survey
7.1 Topographic survey Current river cross-section and topographic data will be required to develop hydrodynamic (1d, 2d and 1d/2d linked model). Latest data will be surveyed during the FFEWS model development period. Existing cross-sectional data in this basin were collected in 2014 (during pre-feasibility, Package 3). Thus, this data are considered old for the dynamic rivers in Nepal; there were additional, but limited, cross-section surveys during the feasibility study (this study, Package 7). In UK, where rivers are very stable, the Environment Agency (responsible for flood forecasting), updates their models if topography is more than 5 years old. Thus, fresh cross-sectional survey, in higher spatial resolution (than in Package 3 and 7) should be undertaken. This will improve model calibration and validation, forecast accuracy, and inundation maps.
On steep slopes and in meandering/braided rivers, cross-sections between 200m and 500m intervals are essential (HEC-RAS, Users’ Manual, Version 4.1, Figure 8-34) for accurate model calibration, validation and forecast accuracy and for inundation mapping. Cross-sections, on average at 380m intervals, are proposed. Rivers in Nepal, typically, have very steep slopes. Although the Terai region is referred to as a flat region with mild slopes, the slopes are still many times steeper than those in low-lying plains. Moreover, rivers are braided and meandering in nature due to high sediment load. As a result, cross-sections in the dense intervals proposed will be useful.
Topographic survey will include river section, any existing structures and flood embankment profile. A survey will have to be done in Lakhandei river and in its two right bank tributaries. In 61km, 161 cross-sections will have to be surveyed. For the topographic survey, no survey equipment has been proposed for purchase; survey will be done through outsourcing.
All cross-sections will cover the river, bank to bank and will be extended into the floodplain to sufficiently high ground of highest historic flood water mark. Horizontal projection for survey will be WGS 84 / UTM zone 45N. Ground elevation will be relative to mean sea level (masl) for controlling vertical datum. All cross-sections shall be connected to Nepal National permanent bench mark for horizontal and vertical datum control. Temporary bench marks (TBM) shall also be established for cross-verification of data. All cross-sections, in a basin, shall be surveyed during dry season, prior to or after monsoon so that cross-sections are stable without much morphological change. All cross-sections shall be surveyed in one season, either prior to monsoon or after the monsoon
Ground elevation (vertical position) in cross-section/topographic survey shall be accurate to better than 20mm in case of level survey and to better than 50mm in case of echosounder depth survey. Horizontal position accuracy should be better than 1m. There should be enough vertical points to sufficiently represent a cross-section shape, dense points about 0.5 to 1m apart at scoured part of the cross-section, and less points over shallow sand bars, about 1 to 5m interval.
Bank/defences survey crest levels are to be provided at intervals that will adequately describe the river bank (typically every 10m).
The following deliverables are required:
Channel sections, longitudinal sections and structure elevation drawings in AutoCAD DWG and PDF format;
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Channel section data in the following formats: o Csv or txt or dat file o AutoCAD; and o Section chainage, X (easting), Y (northing), Z(elevation), distance to next section Width tables for each bridge opening surveyed Cross section location plan in AutoCAD DWG, Shapefile and PDF format; Bank/defence survey location plan in AutoCAD DWG, ESRI shapefile, spreadsheet .csv or Excel 2007 format and PDF format Site photographs: at least 3 photographs per cross-section, taken one looking upstream, one looking downstream and one with another good angle
7.2 Survey budget The budget has been decided based on density (no. of cross-section) of survey, and per cross- section (see Table 17). Topographic survey shall be outsourced and thus no equipment for such survey has been proposed.
Table 17: Topographic survey budget for Lakhandei basin Topographic cross- Lakhandei survey budget (US$) section survey Length of survey No. of XS Total cost Unit (km) cost Lakhandei 46 121 24,200 200 Tributary 1 7.5 20 4,000 200 Tributary 2 7.5 20 4,000 200 Total 61 161 32,200 - Source: Mott MacDonald
7.3 Satellite imagery Budget for purchasing high resolution satellite imageries has been included (Table 18) for Lakhandei basin for lower catchment only in Terai to provide DEM to 1-d and 2-d model development and flood inundation map preparation. High resolution (50cm) Pleiades imageries have been proposed for purchasing. DHM informed that they have already used this imagery in their FFEWS modelling.
Table 18: Satellite imagery purchase budget for Lakhandei basin High resolution (50cm) satellite imagery 2 area (km ) Total cost Unit cost (USD for one sq.km) Lakhandei basin in Terai 316 15,800 50 Total 316 15,800 - Source: Mott MacDonald
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8 Flood forecasting model development
8.1 Mathematical modelling It has been proposed to develop a suite of flood forecasting modelling tools. This includes a simple tool for use in combination with the most advanced hydraulic models being employed in countries like Australia and UK. The hydrological model is an essential (input) component for each of the forecasting tools. The simple tool, gauge to gauge correlation, could be operational within eight months or as soon as some hydrometric data becomes available from the new proposed gauge network. Over a period of three years, advanced hydraulic models will be developed, calibrated, validated and will be made operational as more data becomes available. A conceptual diagram of different components of the models and links between them is presented in Figure 9. The following forecasting tools have been proposed:
Gauge to gauge correlation: the simplest and cheapest method, fast to develop, and thus could be operational within 7 to 8 months from the inception of the project. However, it has a very short lead time (2 to 5 hours) and is also not appropriate in upper steep slope river reaches, as flood wave propagates fast in those reaches and correlation of two gauges is not strong due to presence of pools and riffles. There are also other limitations. Combined rainfall-runoff and gauge-to-gauge correlation: with the addition of a runoff model, the forecast lead time could be extended up to 72hrs. However, this requires a stage-discharge rating curve at each gauging station; such rating curve is difficult to develop for out of bank flow condition without a hydraulic model; as soon as the hydraulic model will be developed (within 9 to 10 months), such rating curve will be available from the model. 1-d model: this tool will be developed for the entire river system in Terai in this basin, and appropriate for flood forecasting; this is the same model type used in Bangladesh. 1-d/2-d linked model: this will be the final delivery month 24; A 2-d model will be transformed into a 1-d/2-d linked model with the linkage to the 1d model.
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Figure 9: A conceptual flow diagram of different components of models, their links and requirement of data
Source: Mott MacDonald
8.2 Rationale for different forecasting approaches The four approaches described above are inter-linked and essential and/or complementing components to the final deliverable/ flood forecasting and early warning system (FFEWS) model, i.e., the 1-d/2-d linked FFEWS model. The rationale, advantages and disadvantages of each approach are described below:
Gauge-to-gauge correlation: is the simplest and cheapest method. This could be an option to use as a quick forecasting tool. It can generate new knowledge, to be translated into the final deliverables (1-d model and 1-d/2-d linked model. Advantages will be that flood forecasting component of CBDRM could be operational earlier and probable areas of uncertainty in flood level forecast could be identified. DHM is using this method in many of their river basins, e.g. in Karnali. This tool and expertise from DHM could readily be used in this basin with some nominal input from innternational consultants; as the tool has to be customised for new basin, need for minorr changes in code and parameters may be required and thus international consultant’s input is considered. There will be a deployment time, in all five basins, for the new hydro- meteorological data to become available, so this work is a good utilisation off the waiting time, as it generates the opportunity of transferring early knowledge to the final product.
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Rainfall runoff model is an important input to all other components: a) gauge to gauge correlation, b) 1-d river model, c) pure 2-d model and d) 1-d/2-d linked model. Combining rainfall model with gauge-to-gauge correlation will increase the lead time (as in the rainfall forecast) up to 24, 48 and 72 hours. However, at the forecasting points, the discharge vs water level rating curve shall be required so that forecasted runoff can be converted to water level using the rating curve. The rainfall runoff model provides inflows from the upper catchment and distributed inflows from intermediate catchments to 1-d, 2-d and 1-d/2-d linked model.
1d model, as a standalone tool, can be applied as a forecasting tool once it is ready. Without the 1-d model, a linked 1-d/2-d model (which is proposed as a final deliverable) cannot be developed. Therefore, it is suggested to develop a 1-d model as forecasting tool as soon as it is ready. In any case, for certain reaches of the river, there will only be a 1-d model, as a 1-d/2-d linked model is not feasible to be developed for the entire reach of the river. This tool will also give useful feedback on forecasting performance, which then could be translated into the final deliverable. In summary, 1-d model development is not a duplicating tool; it is an essential pre-requisite. Should DoWRI and ADB decide not to take forward 1-d/2-d linked modelling, then a 1-d model will be the final product. This is the tool which DHM operate in the Bagmati, Koshi and West Rapti basins. The advantage of a 1-d model is that it runs efficiently, which is a key requirement for real time forecasting. However, a 1-d model does not have direct map output for flood risk or hazard and these would require separate and customised GIS development, e.g., as practiced by forecast model in Bangladesh (http://ffwc.gov.bd/). Such a GIS tool is under development within DHM. It will need to be developed in this project for the 1-d only model reaches of the river.
1-d/2-d linked model is the final deliverable; such FFEWS models are already in operation in countries like Australia, New Zealand, Malaysia and UK (Syme, 2007; Huxley, 2016). Therefore, developing the next generation of the FFEWS tool would ensure that by the time the project is complete, Nepal won’t fall behind on national standards. The 1-d/2-d linked model can forecast flood levels with better accuracy (as it is linked to 2-d floodplain model). Further, flood risk and hazard maps are direct outputs from such modelling. However, run-time is longer than for the 1-d model; it requires more accurate DEM, and therefore, to develop it for all reaches of the river. For selected river reaches, where such modelling will be useful, like in the Lower Terai, this tool shall be developed. To overcome run-time issues for real time forecasting, GPU (graphical processing unit) or HPC (heavily parallelised computing) versions of modelling software shall be used.
In several meetings with DHM, the consultant has proposed development of a similar FFEWS model, with regard to modelling tools and types of models. We have proposed the same type of advanced 1-d model development for FFEWS, which DHM is presently operating in three different basins (West Rapti, Bagmati and Koshi). The same (or similar) modelling software (e.g. MIKE11 and HEC-RAS), for both hydrological and hydrodynamic modelling, has been recommended (in parallel with other software), thus giving DHM wider options to choose from).
8.3 Gauge to gauge correlation The development of a forecasting tool using a gauge-to-gauge correlation has been proposed for Lakhandei River only, where 49km river length with no tributaries will be considered for gauge to gauge correlation (Table 19 and Figure 10). Very upstream and the very downstream water level gauges (newly proposed) in both the rivers will be used for correlation.
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River reaches with relatively mild slope have been considered. The above proposed reach lengths could be changed (decreased or increased) during the development phase after analysing physical data (water level, DEM and cross-sections) when they become available. Reaches with steep slopes have not been considered as in steep slope rivers a downstream gauge has minimum influence to an upstream gauge as the river’s flow regime is mainly flow dominated from the upstream (due to high Froude1 number, i.e., velocity is relatively high see Mott MacDonald, 2018b). Further, the benefit of gauge-to-gauge correlation forecasting is very limited at steep slope reach.
There are existing gauge-to-gauge correlation tools within DHM which are operational in many basins (e.g. Karnali) which will be used. Thus, the new tool can be developed fast and with minimum cost; it will mainly involve analysis and feeding in of the new hydrometric data.
This tool shall be maintained in parallel to advanced 1-d and 1-d/2-d linked models.
Table 19: Proposed river reaches for development of gauge to gauge correlation flood forecasting model in Lakhandei basin River Reach ID Reach Characteristics Channel Slope FF model type length (%) (km) Lakhandei 1 Hill 22 1.71 - 2 Fan 14 0.5 gauge to gauge 3 Peripheral fan 13.8 0.23 gauge to gauge 4 Flood plain, partially 9.6 0.11 gauge to gauge meander / wandering 5 Flood plain, partially 11.1 0.09 gauge to gauge meander Source: Mott MacDonald
1 Froude number (Fr) = u/(gh)0.5 where u is flow velocity, h is water depth and g is acceleration due to gravity
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Figure 10: Proposed river reaches for development of gauge-to-gauge (G2G) correlation flood forecasting model in Lakhandei basin
Source: Mott MacDonald
8.4 Hydrological modelling This section describes development of the hydrrological model. The development of the model involves various systematic analysis:
Seleection of the appropriate precipitation-runoff module; Identification of initial estimates of the model parameters
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Selection of the calibration and validation period covering full range of wet and dry events Calibration of the model Validation of the model Sensitivity analysis of the model parameters required to upgrade the model in future.
The runoff model domain shall cover the entire basin area (425 km2) from Chure Hills to the lower Terai area up to the Indian border in the south.
8.4.1 Review of existing data and models Use the existing hydrological model (Feasibility study model, Package 7) to improve sub-catchment delineation, parameter (calibration) improvement; model parameterisation using local/donor data; probable modelling tools include HEC-HMS and NAM; DHM is experienced with both tools Review available rainfall data from DHM and other secondary sources to provide a representative areal average rainfall Cross-check tipping bucket rain gauge with storage gauge data, including double-mass analysis and / or cumulative-mass time series plots Review data against general meteorological records, in particular to identify periods where there may have been snow / snow melt Comparison of rainfall radar totals with rain gauge information, investigate spatial and temporal distribution of rainfall for selected calibration and validation events Provide a commentary on the suitability of weather radar information to supplement gauge rainfall for rainfall-runoff model development Assess the availability of data, and the uncertainties in the accuracy of the data and what effect this could have on the reliability and accuracy of model outputs Selection of calibration period using long records of meteorological data, (minimum of three to five years). A long period of calibration data is essential due to sensitivity of the runoff model to the initial conditions. Selection of validation period using long records of meteorological data (minimum of three to five years). A long period of validation data is essential due to sensitivity of the runoff model to the initial conditions.
8.4.2 Catchment delineation The basin shall be divided into smaller hydrologic sub-catchments to define catchment topology according to geomorphologic homogeneity. The following will be considered while delineating the sub-catchments:
Topography, DEM based on satellite and their resolution, e.g. SRTM 30m, Cartosat-1 or high resolution Pléiades imageries Drainage network based on satellite imageries Changes in sub-catchment response, key tributaries/confluences, flood storage reservoirs etc. Catchment delineation shall be verified including use of surface water sewer data in urbanised sub-catchments Permanent snowline and snow cover, Soil/sediment and land use data Urbanisation extents: land use in urban areas Embankment layout and location of flood control sluices and structures
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Location of hydrological monitoring sites Average annual precipitation over the basin with reasonably good resolution
8.4.3 Hydrological input: Rainfall, temperature and evapotranspiration Key input data for hydrological modelling are:
Rainfall Temperature Evapotranspiration
Initially the model development shall start with data available through DHM. If quality data within the basin are unavailable, the nearest met station data shall be used. Gridded rainfall data could be used from satellite-based sources (IMD, APH etc.) where available.
As soon as data from new proposed gauging stations become available (should be available after the first monsoon during the development phase of the project), they shall be used for improving both calibration and validation of the hydrological model.
8.4.4 Bias correction Gridded rainfall data cannot be directly used for runoff modelling. Bias correction on historical precipitation series shall be developed for using such data in the FFEWS model.
TRMM-P has the highest possible temporal resolution (three hours) of all gridded precipitation data sources; it is freely available, there is a long record of historical archives, and it is probably the most accurate Satellite-based Precipitation Estimate (SPE) available globally. However, it requires bias correction. Region-specific bias in TRMM-P exists and the bias increases with smaller spatial scales, higher temporal resolution and higher magnitude of precipitation values. It has also been observed that capacity of TRMM-P in resolving orographic precipitation in Himalaya is limited. Thereby, raw TRMM-P has to be bias corrected (eQM) before it can be used for rainfall-runoff modelling.
8.4.5 Calibration The calibration period shall cover hydrological data of at least three hydrological years, but preferably five (or more). Availability of data, particularly rainfall, from different sources has been shown in Section 2.8, Table 9. The years shall be selected judiciously so that observed rainfall and discharge are available at most observation stations, if not at all stations. Missing, inconsistent and erroneous data, and non-availability of data are generally an issue in data collected from existing sources and secondary sources; examples of such data are point and gridded rainfall, temperature, PET, observed discharges. Therefore, these factors shall be considered while selecting the calibration period. In the hydrological model, as the initial condition sensitive, the first 3 to 6 months of simulation period shall be considered as initialisation time, and thus shall be ignored while using the model runoff to hydrological model. One key factor of considering longer simulation period for validation (or calibration) is due to this initial condition effect; other factors are to cover wide range of flow conditions, which will allow low to high range of flows to the hydrodynamic model. Calibration of runoff model against long records helps in finalising catchment parameters. Most hydrological modelling tools, e.g. HEC- HMS, NAM, are conceptual model, and thus, finalising catchment parameters from model runs of short hydrological events (on a scale of week to months) can generate mis-leading catchment parameters in the hydrological model.
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To assess calibration performance between the modelled and observed discharges, scatter plots shall be constructed at all observed gauging stations on matching of peak magnitude and time to peak, also the overall shape of modelled and observed hydrographs. In addition to visual comparison from these graphs, statistical methods shall be used to measure the model’s performance. Example of such statistical methods are:
Nash–Sutcliffe efficiency Coefficient of determination (R2) Volumetric error
8.4.6 Validation Model validation is a process of testing the model’s ability to simulate observed data for a different set of rainfall events than those used in calibration, within accuracy agreed with the client. In model validation, calibrated model parameters shall not be changed; the same set of parameter values used in calibration shall be used during validation. The validation period shall cover hydrological data of at least three hydrological years but, preferably five (please see preceding section on criteria and issues on selecting period of validation).
To assess validation performance, the same procedure as in for the calibration shall be followed; performance shall be checked by comparing the graphics of the peak magnitude and time to peak, as well as comparing the overall shape of modelled and observed hydrographs. The following statistical parameter check shall also be checked:
Nash–Sutcliffe efficiency Coefficient of determination (R2) Volumetric error
8.5 Combined rainfall-runoff and gauge-to-gauge correlation Combined rainfall runoff and gauge-to-gauge correlation will also cover the same river reaches as were covered in the gauge-to-gauge correlation; in Lakhandei, for 49 km river length (see Table 20).
Once the hydrological models are ready, they could be combined with gauge-to-gauge correlation. A ready hydrological model means a calibrated and validated model; if there is need for re-delineation of catchments used in the Package 7 model, then satellite imageries and in- built GIS tool will be used to redefine the watershed boundary; calibration will be carried out using new discharge data proposed for measurement in this study. This will increase lead time to 72 hours (as in the rainfall forecast); however, to utilise the benefit of increased lead time, the stage-discharge rating curve will be required to convert runoff from the hydrological model into the river level at the upstream base station in gauge-to-gauge correlation.
Table 20: Proposed river reaches for development of combined rainfall-runoff and gauge to gauge correlation flood forecasting model River Reach ID Reach Characteristics Channel Slope FF model length (%) type (km) Lakhandei 1 Hill 22 1.71 RR river and its 2 Fan 14 0.5 RR tributaries 3 Peripheral fan 13.8 0.23 RR+gauge to gauge 4 Flood plain, partially 9.6 0.11 RR+gauge
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River Reach ID Reach Characteristics Channel Slope FF model length (%) type (km) meander/wandering to gauge 5 Flood plain, partially 11.1 0.09 RR+gauge meander to gauge 4 Meander (Lakhandei 9.77 0.1 RR+gauge confluence (Nepal-India to gauge border) Source: Mott MacDonald
8.6 Pure 2-d modelling No pure 2-d modelling has been proposed in this basin; pure 2-d modelling was proposed only in Mohana-Khutiya and in Mawa-Ratuwa for capacity building within DHM; this learning is essential for developing the 1-d/2-d linked models. In this basin and in other basins, 2-d model will be developed through 1d/2d linked modelling.
8.7 1-d modelling All reaches of the Lakhandei River (49 km), from immediately downstream of Siwalik hills to the Indo-Nepal border in the south, will be included in the 1-d modelling; additionally two right bank tributaries (7.5 km each) have also been included; total 61km reach has been proposed (Table 21 and Figure 11).
The model shall be calibrated and validated for the same hydrological events as mentioned for hydrological modelling (Section 8.4).
8.7.1 River network A 1d modelling/forecasting approach has been proposed for Lakhandei river including two of its tributaries.
The river reaches in the Terai, as shown in Figure 1 and Figure 2, shall be considered for the 1- d river modelling. Runoff from the catchment in the Siwalik Hills shall be routed to the 1-d river model by hydrological modelling. Details of reaches and catchments considered in rainfall runoff (RR) and 1d river modelling are presented in Section 8.7.1.
If during the development phase it will be deemed appropriate, depending on topography and channel density in the Siwaliks, then flood routing, e.g. based on Muskingum-Cunge, will also be considered. Such hydrological routing may improve flood attenuation, flood volume and flood travel time to the downstream 1-d model in the Terai. The above proposed reach lengths in the 1-d model shall be fine-tuned during the development phase depending on the field conditions as more physical data (water level, discharge, DEM and cross-sections) become available.
A single fluvial model shall be built considering all the main rivers and their tributaries included in the same model set-up. The model, with all interconnected branches, will deliver better results. None of the tributaries/branches shall be built as separate 1-d models.
Modelling approach shall be submitted for acceptance by the client (i.e., DHM) before model build commences.
Key characteristics of the model shall include:
Distributed inflows to reflect the key hydrological characteristics of the catchment
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All structures which influence flood flows/levels between 50% (generally bankfull discharge) and 0.1% AEP plus climate change allowance All flood defence work All model nodes and units are to be geo-referenced, to true geo-graphic co-ordinates (i.e. schematic set-up of model units shall not be accepted) Channel (1-d), bank to bank Floodplain, as extended section in 1-d, as flood cells connected to the river (1-d) or floodplain as separate channel and connected to main channel
Table 21: Proposed river reaches for development of combined rainfall-runoff and 1-d modelling for flood forecasting model River Reach ID Reach Characteristics Channel Slope FF model: gauge length (%) to gauge (km) correlation Lakhandei 1 Hill 22 1.71 RR 2 Fan 14 0.5 RR+1-d model 3 Peripheral fan 13.8 0.23 RR+1-d model 4 Flood plain, partially 9.6 0.11 RR+1-d model meander/wandering 5 Flood plain, partially meander 11.1 0.09 RR+1-d model Note: 1-d model shall also include part length of two right bank tributaries (each 7.5km) in the 1-d model (cross-section surveys have been included for this) Source: Mott MacDonald
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Figure 11: One-dimensional (1-d) model domain in Lakhandei basin showing proposed reaches within 1d domain
Source: Mott MacDonald
8.7.2 Calibration and validation The model shall be calibrated and validated for the same hydrological events as mentioned for hydrological modelling (Section 8.4). To assess calibration performance between the modelled and observved discharges, the same statistical methods as mentioned in Section 8.4 shall be applied.
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8.8 1d/2d linked modelling Flat reaches in the Terai area shall be transformed to the 2-d model and then linked with the 1-d model. Total length for 1-d/2-d linked model shall be 21km (Table 22 and Figure 12). The advantage of pure 2-d and 1-d/2-d linked modelling will be the generation of flood outlines as direct output from the model results, whereas in 1-d modelling, flood outlines have to be generated separately using 1-d model results and floodplain DEM.
Table 22: Proposed river reaches for development of combined rainfall-runoff and 1-d/2-d linked modelling for flood forecasting in Lakhandei River Reach ID Reach Characteristics Channel Slope FF model: gauge length (%) to gauge (km) correlation Lakhandei 1 Hill 22 1.71 RR and its 2 Fan 14 0.5 RR+1-d model tributaries 3 Peripheral fan 13.8 0.23 RR+1-d model 4 Flood plain, partially 9.6 0.11 RR+1-d/2-d model meander/ wandering 5 Flood plain, partially 11.1 0.09 RR+1-d/2-d model meander Source: Mott MacDonald
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Figure 12: One-dimensional (1d) and 1d/2d linked model domain in Lakhandei basin showing proposed reaches of model domain
Source: Mott MacDonald
8.9 Operation of forecasting model
8.9.1 Key tasks Operating the forecast model on a continuous basis will involve automation; the main activities would probably include:
Writing scripts/program which will auutomatically download forecasted rainfall from WSF/WRF/IMD; the frequency of downloading shall be discussed and agreed with
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DHM; if forecast is issued three times during the day, the downloading will be at the same frequency Writing scripts/program which will convert forecasted rainfall into format which is compatible as input to runoff modelling tool, e.g., in case of NAM, it should be a dfs0 file while in HEC-HMS, it should be a HEC-HMS-DSS output file Writing scripts/program which will download real time discharge and water level from DHM’s central server, convert the data into HEC-RAS or MIKE11 compatible format; this data will be used for the forecast; forecast run is usually for 7 days duration – 4 days of hindcast whose performance is verified using real time water level and discharge and 3 days (72 hours) of forecast run. Writing a scripts, which will identify missing and erroneous data, particularly for rainfall data, which is used as input to hydrological model for generating run-off, and which are then input to the hydrodynamic model; both erroneous and missing rainfall record shall be replaced with data from other sources (e.g., from neighbouring station/grid) Writing script/program which will trigger automatic run of hydrological and the hydrodynamic models, for the same number of times during a day as agreed with DHM Writing script/program which will extract output (water level and discharge, and flood inundation map) in graphical formats at all forecasting points.
8.9.2 Real-time data transmission and maintenance Maintaining a central database server for telemetric data and also for near real time data is essential. This study will utilise the existing real time data management system (Figure 13) within DHM for data transmission to the central server, analysis and preparation of input for the model run. Input by International and National Experts have been kept for integration of telemetric data from the proposed new telemetric gauge network to DHM’s system.
Key elements for real time data transmission and management involve:
Operation of telecommunication system: this is outsourced and supervised by DHM Processing of data received from telemetered gauges by Flood Forecasting Centre, DHM Processing of data received from manual gauges by Flood Forecasting Centre, DHM
During forecast run, the hydrological model and hydrodynamic models use following data:
Forecasted rainfall from weather forecast model Real time rainfall, water level and discharge data from the telemetric gauging network
During each forecast run, daily once (or more), the model will run for a 4-day hindcast period and a 3-day forecast period. For the hindcast part of the simulation, input data (rainfall, water level and discharge) should be real-time data, which may also be supplemented by TRMM gridded rainfall data. Running of the hydrological and hydraulic models during forecast season is carried out Flood Forecasting Centre, DHM. The 7-day run time only in 1d only model will be quite fast requiring about 30 minutes for pre- processing, model run time and post processing. In case of 1d/2d linked model, the run time is expected to be higher, between 70 and 75 minutes. However, this run time will depend on the type of computer being used, and spatial resolution of both 1-d and 2-d model.
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Figure 13: Real time data management and transmission system within DHM
Source: DHM (Courtesy Mr Binod Parajuli, Hydrologist/Forecaster, Flood Forecasting Section)
8.9.3 Existing forecast model operating system within DHM DHM’s existing operating system (Figure 14) will be used for operation and forecastiing from the new models.
To operate a forecasting system using models developed in HEC-HMS & HEC-RAS, and NAM & MIKE11, the pre-processing and post processing work for input preparation and output derivation are relatively easy. In these two cases, the hydrological model (HEC-HMS or NAM) and hydrodynamic model (HEC-RAS or MIKE11) are coupled. Thus, writing any codde/script/tool for these two systems for pre-post processing purpose requires minimum effort.
However, there are forecasting models which use uncoupled hydrological and hyydrodynamic models, e.g., PDM and Flood Modeller Pro used widely in UK, and URBS and TUFLOOW used in Australia. In cases like these, relatively consideerable works are required for such automation in preparing input data and deriving outputs while running the model in real time. However, there are also tools available for such purpose, e.g. Delft-FEWS. A separate section is added on Delft-FEWS (See Section 8.9.4)
DHM already has HEC-HMS and HEC-RAS, and NAM and MIKE11, models operatioonal in their two major basins (Kosi and Babai). As a ressult, we have proposed minimum input for the consultants for developing an operating system for the forecast models. DHM’s existing system will be used. Input by international and national consultants has only been kept to integrate the new basin models from this study to DHM’s exissting forecasting system.
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Figure 14: DHM’s existing flood forecasting and dissemination system
Source: DHM / ICIMOD
8.9.4 Delft-FEWS Delft-FEWS provides an open shell system for managing forecasting processses and/or handling time series data (https://publicwiki.deltares.nl/display/FEWSDOC/Home). The Delft-FEWS forecasting system was essentially built as a shell around the hydrological and hydraulic models used (Werner et al., 2012). The system contains no modelling ccapabilities (rainfall-runoff and hydrodynamic modelling) within its code base. Instead, it enntirely relies on third party modelling components for rainfall-runoff and hydrodynamic modelling. The structure of the Delft-FEWS includes a data storage layer, a data access layer, as well as several components for importing, manipulatinng, viewing and exporting data. The structure of Delft-FEWS is shown in Figure 15. Currently Delft-FEWS is used in over 40 countries. Delft-FEWS can either be deployed in a stand-alone, manually driven environmment, or in a fully automated distributed client-server environment.
DHM has an existing operation system for FFEWS for models, e.g., Kosi and Bagmati by hydrological and hydraulic modelling, and in Karnali and Narayani by probabilistic modelling, (DHM, 2018). Similarly, Bangladesh and India (Bihar), in their flood forecasting models developed in NAM and MIKE11, and HEC-HMS and HEC-RAS, also use their own operational system.
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Figure 15: Schematic structure of a flood foorecasting system, showing the pposition of Delft-FEWS and links to other primary systems within the operational environment
Source: Werner et al. (2012)
8.9.5 Dissemination of forecast Dissemination of forecast will involve automation; main activities shall include:
Writing script/program, which will disseminate forecast including graphical outputs to all designated recipients including CBDRM committee and other stakeholders.
8.9.6 Data assimilation Data assimmilation is a technique for combining any measurements of the state of the system with the moodel dynamics in order to improve thee knowledge of the system. The data aassimilation technique can be applied by incorporating observed water level and discharge meaasurements into the hydrodynamic model. Furthermore, the data assimilation module can be used for uncertainty assessment.
The flood forecasting methodology to be appplied in the FFEWS development in this basin should have the ability to assimilate real time/near real time telemetry observations of flow and water level. This will allow the updating of model results in real time and improve aaccuracy of the forecast.
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Data assimilation needs to be applied on the following forecast models on discharge and water level where real time telemetry data are available:
Combined rainfall-runoff and gauge-to-gauge correlation model 1-d hydrodynamic model 1-d and 2-d linked hydrodynamic model.
8.10 Evaluation of forecast The forecast issued during the first, second and third year shall be evaluated. Based on the evaluation, follow-up (continuous) model update shall be recommended and implemented within the duration of this project, while the knowledge transfer to DHM shall be ensured for future update, operation and maintenance of the following forecast models:
Gauge-to-gauge correlation Combined rainfall-runoff and gauge-to-gauge correlation model, within the duration of this project 1-d hydrodynamic model, within the duration of this project 1-d and 2-d linked hydrodynamic model, within the duration of this project
Forecast evaluation should be carried out using the Skill Scores as per the criteria described in WMO’s Manual on Flood Forecasting and Warning (WMO, 2011).
8.11 Model development schedule The suite of forecasting models shall be developed and made operational over a period of three years (Figure 16), represented by sub-programmes to be completed in 36 months in nine periods (PR1 to PR9, one period is 4 month); this 36 month period is deemed essential as the model will use new data from the proposed gauging network and topography including DEM from high resolution (50cm) satellite imagery.
Gauge-to-gauge correlation can start fairly early as soon as some water level data are available from the new proposed water level gauges; it is noted again that there is no existing hydrometric network in this basin; Hydrological modelling will also be started from the start of PR2 by using third party and existing rainfall data. As soon as rainfall data from proposed new rainfall gauges are available the model will be updated, calibrated, validated and improved with new rainfall data and discharge data; Forecast issuing will immediately be started using the gauge-to-gauge correlation approach (which has limited lead time). In parallel, as soon as the RR model is ready, the RR model will be combined with gauge-to-gauge correlation forecasting; combining with the RR model will give power to gauge-to-gauge correlation to forecast with much higher lead time (up to 72 hours).
Parallel to the above modelling, 1-d, 2-d and 1-d/2-d linked model development will continue; this advanced modelling is more data dependent, particularly on topographic data. 2-d modelling will be carried out as a pilot exercise and for capability development. This experience will be used in 1-d/2-d linked model development. Forecasts, however, will be issued using the 2-d model for the domain where the model is developed. The 1-d forecast model will be operational from PR5 and the 1-d/2-d linked model will be operational from the middle of PR7
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Figure 16: Flood forecasting model development programme for Lakhandei basin
8.12 Model development budget Based on the modelled river length and catchment size, unit cost has been derivedd (see Table 23). Similar unit costs are applicable in the UK and Ireland (Environment Agency, 20115).
In the UK, hydrological model development cost is $500 to $1500 per km2 of catchment; the cost considered here is relatively low because the hydrological model already exists in these five sub-basins (from pre-feasibility and feasibility level). This advantage will reduce development cost during FFEWS.
Table 23: Forecasting model development budget for Lakhandei basin Categories Parameter Unit Quantity Developm Annual Annual Unit ent cost: costt: cost Operation Dissse ($) minati on Data: collectionn, Per basin No. 1 32,500 - - 32,500 processing, analysis Hydrological modelling Catchment km2 425 80,500 - - 189 area Gauge to gauge River length km 49 52,000 13,575 11,700 1,061 correlation Pure 2-d modelling River length km 0 0 0 0 0 1-d modelling River length km 61 68,000 24,000 13,875 1,063 1-d/2-d linked River length km 11 80,500 24,000 11,175 7,318 modelling Modelling software Suite No. 1 13,000 - - 13,000 Note: Modelling software license cost is distributed over five basins; software will have multi user network license, and cost shown here is per basin. West Rapti is exclude from software cost Source: Mott MacDonald
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8.13 Person-months for experts The suite of FFEWS tools shall be developed for this basin following development of each tool in the Mohana-Khutiya basin, and thus, expert input in this basin shall be less (Table 24).
To consider person-months for experts, one key assumption is that the Mohana-Khutiya basin FFEWS model development will start first, and thus, will require higher expert inputs. The next basins shall be Mawa-Ratuwa. Thus, the other basins will benefit from the experience gained from these two basins; as a result, the other three basins will require fewer person-months. Catchment size and river lengths were also key factors in deciding person-months. With experience, the input from international experts will decrease in other basins than Mohana- Khutiya and Mawa-Ratuwa.
In developing FFEWS in five basins, the inputs of three international experts and four national experts have been considered over a period of three years; one GIS cum data expert (National) has also been considered to support the team.
The discipline of both international and national experts shall be:
International
Senior/Principal Hydraulic & flood forecasting modelling expert Hydraulic & flood forecasting modelling expert Hydrologist & flood forecasting modelling expert
National
Hydraulic & flood forecasting modelling expert-I and expert-II Hydrologist & flood forecasting modelling expert-I and expert-II GIS cum data analysis expert
Following activities could easily be applied/customised from the work of Mohana-Khutiya and Mawa-Ratuwa basin where the modelling work is assumed to start first, so the cost for these activities will be relatively low in this basin, only for these activities:
Detailed model development conceptualisation document (according to feasibility document) shall be developed first in the Mohana-Khutiya basin, which shall be customised and copied to this basin Training and capacity building for 1-d, 2-d and 1-d/2-d linked modelling will be offered to national experts and DHM professionals during tool development in the Mohana- Khutiya basins, and thus the basin will have input from more experienced experts A script for automation of FF model runs in real time shall be developed first for Mohana-Khutiya basin, and can be adopted to this basin with very minimum input A script for automation of forecast dissemination in real time shall be developed first for Mohana-Khutiya basin, and can be adopted to this basin with minimum input
Considering the above activities, the person-months for Lakhandei have been calculated as shown in Table 24 below.
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Table 24: Experts’ man-month for Lakhandei basin Development phase Operational phase Dissemination Models: Lakhandei Inter- Inter- Inter- National National National national national national Data: collection, 0.5 1 processing and analysis Hydrological 2.4 4.8 Gauge to gauge 1 3 0.25 0.8 0.2 0.8 correlation 1d modelling 1.5 4 0.5 1 0.3 0.5 Pure 2d model 0 0 0 0 0 0 1d/2d Linked Modelling 2 4 0.5 1 0.2 0.7 Total 7.4 16.8 1.25 2.8 0.7 2
Source: Mott MacDonald
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References
[1] Allot, T (2010), The British Rainfall Network in 2010, https://www.rmets.org/sites/default/files/pdf/presentation/20100417-allott.pdf
[2] Berndtsson, R and Niemczynowicz, J (1988), Spatial And Temporal Scales in Rainfall Analysis Some Aspects And Future Perspectives, Journal of Hydrology, 100 (1988) 293-313
[3] DHM (2018), Standard Operating Procedure for Flood Early Warning System in Nepal
[4] DoWRI (2016), Package 3: Flood Hazard Mapping and Preliminary Preparation of Flood Risk Management Projects, Final Report – VOLUME 1, Prepared by Lahmeyer International in association with Total Management Services
[5] EA/Defra (2013), Benchmarking the latest generation of 2D hydraulic modelling packages, Report – SC120002
[6] EA/Defra (2004), Benchmarking of hydraulic river modelling software packages, Project Overview, R&D Technical Report: W5-105/TR0, URL for this research is below:
(https://consult.environment- agency.gov.uk/engagement/bostonbarriertwao/results/appendix-6---neelz--s.--- pender--g.--2013--benchmarking-the-latest-generation-of-2d-hydraulic-modelling- packages.-bristol_environment-agency.pdf)
[7] Environment Agency (2015), Cost estimation for flood warning and forecasting – summary of evidence, Report –SC080039/R13
[8] Huxley C (2016), GPU – Next Generation Modelling for catchment Floodplain Management, BMT-WBM, ASFPM Conference
[9] 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
[10] Lengfeld et al. (undated), Pattern: Advantages of High Resolution Weather Radar Network, American Meteorological Society 36th Conference on Weather Radar Networks
[11] Mott MacDonald (2018a), Morphology Assessment: Mohana – Khutiya basin, WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal
[12] Mott MacDonald (2018b), River Hydrology Assessment: Mohana – Khutiya basin, WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal
[13] NIWA (2014), Climate Manual, National Institute of Water & Atmospheric Research Ltd
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[14] Schaake, J., 2004. Application of prism climatologies for hydrologic modelling and forecasting in the western U.S. In Proceedings of 18th Conference on Hydrology. Seatle, Washington, 2004. American Meteorological Society
[15] Smith, P.J., Brown, S and Dugar, S (2017), Community-based early warning systems for flood risk mitigation in Nepal, Nat. Hazards Earth Syst. Sci., 17, 423–437
[16] Syme, B (2007), 2d and 1d/2d modelling, BMT WBM
[17] Volkman T. H.M., Lyon, S. W., Gupta, H. V. and Troch, P. A. (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
[18] Werner et al. (2012), The Delft-FEWS Flow Forecasting System, Environmental Modelling and Software, 40 (2013), 65-77
[19] WMO (2011), Manual on Flood Forecasting and Warning, WMO-No. 1072
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Appendices
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A. Modelling software comparison
Flood Infoworks FEATURE DESCRIPTION MIKE Modeller SOBEK HEC TUFLOW WEAP MODSIM RIBASIM ICM Pro
GENERAL
Single software suite No interface problems; x x x x x x x one supplier for support
Track record of Ensure for foreseeable support future
GIS Based Spatial information x essential
OpenMI compliant Linkages to external x x x x x x x software
Local familiarity Local support in Nepal x x x x x x x
User and Reference Scientific background and Manuals user interface.
Established training Regular training courses x x courses
Graphical interface Data entry and visualisation
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Flood Infoworks FEATURE DESCRIPTION MIKE Modeller SOBEK HEC TUFLOW WEAP MODSIM RIBASIM ICM Pro
HYDROLOGY NAM - - HEC-HMS - WEAP MODSIM ?
Snow and Glacier Melt Runoff from snow and x x x x x x glacial melt
Rainfall-Runoff Runoff from rainfall. x x x
Auto-calibration Automatic adjustment of x x x x parameters
Infoworks HYDRAULICS MIKE 11 ISIS SOBEK HEC-RAS TUFLOW ICM
Full hydrodynamics Full hydrodynamic analysis
Structure operations Structures and controls
Inflow and Flood Advanced data x x x x x x x x forecasting assimilation
Optimisation Optimal operation of x x x x x x x x system controls
Auto-calibration Automatic adjustment of x x x x x x x x parameters
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Flood Infoworks FEATURE DESCRIPTION MIKE Modeller SOBEK HEC TUFLOW WEAP MODSIM RIBASIM ICM Pro
Sediment transport Sediment transport (optional)
Water quality Transport and decay of (optional) substances
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B. Comments and responses
Table B.1: Comments on FFEWS Reports from ADB Comments from ADB were generic for the reports five basins: Mohana-Khutiya, Mawa-Ratuwa, Lakhandei, Bakraha and East Rapti and Lakhandei.
Comments were received in a MS Word file which are presented in Table below
Comments were also received on the hard copy of the Mohana-Khutiya (M-K) Report on each chapter (chapter 0 to 8). Those comments, though made on M-K report, are mostly generic and applicable for all the other four basins. All comments made on the hard copy have been addressed in all five basin reports and reports have been updated accordingly. The changes made are available on track changes mode. For some comments, there was need for a response for the ADB reviewer; those responses are made on the pdf version of each chapter.
Responses for these comments are presented below.
Reference Comments from ADB Reply consultant
1 Language: English needs to be improved. All suggestions made on the marked up copy of M-K Report have been addressed; Repetition of text is observed at many places.. removed repetition at places and have also improved English. Suggestions have been provided on a marked- up copy of the Mohana-Khutiya FFEWS report. Existing hydromet data : Provide an overview 2 We have provided an overview in Table 9 in chapter 2. table of the existing and forecast hydromet data (rainfall, water-levels, flows, temperature and evapotranspiration), including; o Type & source
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Reference Comments from ADB Reply consultant
o Location o Period available o Collection method o Collection frequency o Transmission method o Latency o Where it is stored, etc.
3 Provision of X-band Radars: It has been found Agreed; that DHM (under the World Bank’s PPCR We have dropped this item from budget; however, we have kept the write-up in Section 5.3, if project) has installed a C-band radar at Surkhet, DHM wishes to consider them in near future. Within the texts, we have made this clear that X- which covers an area with a radius of 250 km. band radar will not be considered in this project, and thus, no budget has been included. Similarly, DHM is in the process of installing 2 However, we want to mention that the C-Band long range radar, which DHM is in the process of more C-band radars at Central and Eastern installation at three locations, may not be operational or data may not be available during next 2 Nepal, and several x-band radars. The C-band to 3 years, by which time this project may be completed. radars will cover the entire Terai and the X-band radars will cover inner valleys. Therefore, there We also want to mention that total 3 nos. of C-band radar across Nepal will provide a density of one radar per 49,060 km2 in Nepal, while such density, for example in UK, is one C-band radar is no need of procuring the 5 X-bad radars. 2 per 14, 264 km . Therefore, DHM/ADB, if wishes in future, can also consider short range radar installation.
4 Provision of ADCPs: DHMs has already We have modified the discharge measurement equipment list, however, a bit different awarded the procurement of 5 ADCPs (under than ADB’s suggestion. the World Bank’s PPCR project). Out of these 5 We have proposed three set of equipment; please see our considerations: ADCPS, DHM plans to provide one each to its basins offices at Biratnagar, Pokhara, Bhairawa Mohana-Khutiya: one set of equipment for this basin alone and Kohalpur, so that these basin offices will be (This basin, in far west Nepal, is far away from the other five basins; this basin has responsible for measuring discharge in all the 120discharge measurements during three years; thus it will be very difficult for this rivers of Nepal. To supplement DHM’s ADCPS, basin to share its equipment with another basin. Similarly, it will also be difficult to
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it might be useful to provide two more ADCPs borrow DHM’s equipment for 120 measurements; for training and as spares for measuring Mawa-Ratuwa and Bakraha: one set of equipment for these two basins discharge in the priority basins. Therefore, it is Mawa-Ratuwa and Bakraha will have 30 measurements per year for each suggested that the consultant modify the list of basin (total 60 measurements in two basins), and these two basins are side equipment. by side, and thus sharing one equipment set in this basin is possible. Thus, we have proposed one equipment set for these two basins
West Rapti: we are not proposing any equipment set. DHM’s equipment will be used in this basin East Rapti and Lakhandei: we are proposing one equipment set for these two basins; this set will be shared between the two basins; however, occasionally, there may be need to borrow DHM’s equipment in crisis management. There will have 30 measurements per year for each basin
5 Discharge measurement : The consultant is We have further described the approach in Section 6.2.2 for ADCP measurements, suggested to describe the discharge measuring Section 6.2.3 on cableway discharge measurements, and Section 6.3 in measurement approach in the six basins (including the West frequency. Rating curves in Section 6.3, para 3. Rapti River) using the ADCPs. The approach Involvement of DHI basin office: should include frequency, use of boats or cableways and development of rating curves. Agreed; in all discharge measurement, technical professional from basin/regional Also, consultant should describe the office of DHM will be involved; we have updated texts accordingly in Section 6.2.2, in involvement of DHM’s basin offices with a focus the para below the bullet points on capacity building. 6 Discharge stations : Streamflow gauging and We have included sufficient number of stream flow and rating curve stations. Here, we rating curves are proposed for only some water- will have thee discharge stations (one existing and two new) in approximately 59km level stations. Provide explanation for why the reach of the Mohana. In Bangladesh and India, discharge measurement stations are water-level-only stations will not be gauged. at far distances than this.
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If no new tributary is joining the main river or if the intermediate catchment is not large enough, then there is no need to add new discharge station; all water level stations do not need to have flow gauging as well; this is the practice around the countries, like I India, Bangladesh, UK and in many other countries.
7 Calibration/validation data : The calibration & We have provided an overview of data availability in Table 9 in Section 2. And all validation sections of the reports mention the hydromet network has been proposed for installation within first six months, so that need for calibration/validation data, but for many proposed modelling can use the data from the first monsoon for calibration and basins such data are unavailable. Linked with validation. the above bullet point, provide a summary of the existing data available for calibration/validation. If no data are available, then the basin’s works programme should reflect the need to start collecting data early for subsequent use in calibration/validation. 8 Topo survey : Explain why additional x/s survey We have explained this in the report in Chapter 7. This survey is required for all three is required. Is this required for; bullet points as mentioned, and as well we should replace all cross-sections which were surveyed in 2014; these will be more than 5 years old. In UK, where rivers are o Accuracy of level forecast very stable, the Environment Agency (responsible for flood forecasting) updates their o Accuracy of inundated area forecast model if topography is more than 6 years old. Here in Nepal, we need such update earlier as the rivers are morphologically dynamic. We also need more cross-sections o Rating curve extension (by hydraulic in Teari to develop 1d/2d linked model and 2d model. model) 9 Topo survey : Will the x/s survey include the Yes, cross-sections will be extended to floodplain. It has already been mentioned in floodplain, if so then state. the Executive summary; we have now also mentioned this in chapter 7 as well. 10 2D modelling – DTM : The proposed hydraulic Thanks for this comment and coming up with your support to include some purchase model methodology includes 2D modelling for of better DTM by diverting the fund of X-band Radar. some areas. Previous work has shown
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Reference Comments from ADB Reply consultant
incompatibility between the available SRTM- We have included now the purchase of PLEIADES satellite imagery, which DHM has derived DTM and the surveyed cross-sections. already used (informed by DHM Flood forecaster); this imagery is available upto 50cm The report does not outline how this resolution. incompatibility will be resolved either by (i) obtaining a new DTM from alternative satellite The proposed topo and cross-section survey in each basin will have more cross- sources, or (ii) surveying the floodplain, by sections in the flat Terai region. This was particularly planned where 2-d model will be LiDAR or traditional methods. If it’s is proposed built and 1-d/2-d model will be linked. This was already mentioned in the report. to use the existing SRTM derived DTM then The purchase of the new DTM (PLEIADES) will help developing a better and accurate consideration should be given to whether the 2-d model in combination with cross-sectional data inaccuracies in the DTM are commensurate with the improved accuracy of the 2D approach. The consultant may recommend to carry out topo surveys of the flood affected area using LiDAR or another modern method. Explanation may also be included on the use of accurate DTMS for Irrigation infrastructure planning by the Government of Nepal. Additional cost of topo surveys may be covered from the cost allocated to X-band radars and ADCPS. 11 2D modelling – computational time of real time Summary: total time for issuing forecast will be about 70 to 75 minutes for a basin. inundation forecasting: Many of the basins are Forecast model operations are described in details in Section 8.9. fast responding (12-24 hours) and forecasts will 30 minutes of data processing and analysis, 30 minutes of model run time and 15 need to be issued without unnecessary delay. minutes for disseminating the forecast. Operational phase will be relatively easier, for The report does not outline the estimated which DHM has already existing system (similar to Delft-FEWS); so hopefully there increase in computational time required to will not much issues at operational and dissemination phase. There should have 2 to 3 undertake the 2D modelling and whether the operators (technicians) to do this routine process each day during forecast season time increase is practical for these fast (monsoon). And consultant (at least one international and one national) from this responding catchments. project will remain available full time for three years. The core work of this project is the development of the models (runoff, 1d, 2d and
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1d/2d linked models). In this note, we want to mention that there are 2d modelling, for flood forecasting, in use in India (Bagmati River in Bihar) and Australia, where run-time in real-time is around 30 minutes (we have referred the literature, have mentioned it at a number of places within the report (Huxley, 2016). Once the forecasted rainfall will be received on a day and real time water level and discharge data will be received, after processing the data (which will also be automised like in Delft-FEWS) for model run (runoff model and hydrodynamic model), the models to complete run will take about 30 to 40 minutes (for all five basins, running from a batch file).
12 Flood forecasting system : The reports Distinction between Hydrological and hydraulic model: (especially App A) don’t make a clear distinction Yes, this is correct, in Appendix A, we mainly wanted to list the hydrodynamic between a hydrological/hydraulic modelling modelling software, and then have added in any of the hydrodynamic software, there system and a flood forecasting system. The is a coupled hydrological software. In the list of modelling software (see Table 11), we flood forecasting system is the tool which wanted to mention key and benchmarked hydrodynamic modelling software only, and integrates real time data, conducts model runs wanted to include those which DHM uses at present. and creates flood forecasts and warning including Web publications and SMS alerts. A There is no bench marking research (to my knowledge) for hydrological modelling Forecasting system generally need to carry out software. However, we have described three key hydrological modelling software: the following activities; NAM, HEC-HMS and PDM among which NAM and HEC-HMS are being used by DHM. We did not aim to describe all hydrological and hydrodynamic modelling o Read observed hydromet and rainfall software available around the world. We are afraid, we will struggle with our allocated forecast. input. o Quality assurance on observed and
forecast input data Flood forecasting system: o Determine how to interpret poor quality or missing data (ie rainfall hierarchy) We have now added on Forecasting system/Tool in Section 8.9.2 to 8.9.4 and Delft-
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FEWS in Section 8.9.4 o Prepare model input files, including boundary condition and hotstart files Bulleted items mentioned here, like : Read observed hydromet and rainfall forecast, Quality assurance on observed and forecast input data, data assimilation are o Schedule, distribute amongst computing mentioned in Section 8.9. resources and launch simulations
o Carry out data assimilation Now, please see below bullet wise response: o Extract relevant simulation results Read observed hydromet and rainfall forecast. o Determine status during forecast period Please see bullet 1, 2 and 3 in Section 8.9.1 o Prepare and issue warnings Quality assurance on observed and forecast input data o Disseminate warnings to Web and create SMS alerts. Please see bullet 4 in Section 8.9.1 o Archive results Quality assurance on observed and forecast input data Please see bullet 4 in Section 8.9.1 Prepare model input files, including boundary condition and hotstart files Please see bullet 2 to 5 in Section 8.9.1 Carry out data assimilation Please see Section 8.9.6 Extract relevant simulation results Please see bullet 6 in Section 8.9.1 Determine status during forecast period Not understood, what is meant by this comment Prepare and issue warnings
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Please see bullet 1 in Section 8.9.5 Disseminate warnings to Web and create SMS alerts. Please see bullet 1 in Section 8.9.5
13 Flood forecasting system : Outline the We have now mentioned the functionality of Delft-FEWS and use of the existing difference between an open source software forecast model operation system within DHM ( see section 8.9.3 and 8.9.4) which requires coding these features, and a
proprietary software which includes most of the in-built functionality for these features 14 Flood forecasting system: Provide a short Provided, see Section 8.9.4 overview of Delft FEWS and the fact that it can Also provided below for ready reference incorporate many different hydrological and hydraulic models, including HEC and MIKE. Delft-FEWS provides an open shell system for managing forecasting processes and/or handling time series data (https://publicwiki.deltares.nl/display/FEWSDOC/Home). The forecasting system was essentially built as a shell around the hydrological and hydraulic models used (Werner et al., 2012). The system contains no modelling capabilities (rainfall-runoff and hydrodynamic modelling) within its code base. Instead, it entirely relies on third party modelling components for rainfall-runoff and hydrodynamic modelling. The structure of the Delft-FEWS includes a data storage layer, a data access layer, as well as several components for importing, manipulating, viewing and exporting data. The structure of Delft-FEWS is shown in Figure 15. Currently Delft-FEWS is used in over 40 countries over the world. Delft-FEWS can either be deployed in a stand-alone, manually driven environment, or in a fully automated distributed client-server environment. DHM, in their existing FFEWS, (e.g., in Kosi and Bagmati by hydrological and hydraulic modelling, and in Karnali and Narayani by probabilistic modelling) uses their own operational system (DHM, 2018).
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Reference Comments from ADB Reply consultant
Similarly, Bangladesh and India (Bihar), in their flood forecasting models developed in NAM and MIKE11 and HEC-HMS and HEC-RAS, also use their own operational system. 15 Operational FFEWS : Provide a summary We have incorporated this comment in chapter 8 in details. overview of how each FFEWS will operate, in Coverage (hydrological, 1D hydraulic, linked 1D-2D hydraulic) terms of; Please see Figure 10 to 13 o Coverage (hydrological, 1D hydraulic, linked 1D-2D hydraulic) All water level and water and discharge gauges are forecast points; these will be the forecast points at gauged locations. At un-gauged locations, each computational node o Input data (observed) and topo (x/s & will be a forecast point, approximately 300 to 400m apart along the river 2D). What will the gridded meteorological data be used for ? Input data (observed) and topo (x/s & 2D). What will the gridded meteorological data be used for ? o Quantitative precipitation forecast (QPF), also considering DHM’s WRF model Gridded data will be used from APHRODITE, TRMM and IMD (in case of IMD, DHM results will require a treaty with India for using their data) o Frequency of forecast, Frequency of forecast o Hindcast and forecast horizon, Daily once or more during high or multiple peak, please bullet 5 in Section 8.9.1 o Forecast points, Hindcast and forecast horizon o Forecast deliverables Seven days: 4 days for hindcast and three days for forecast. Please bullet 3 in Section 8.9.1 o Timing, including latency, pre- processing, runtime, post-processing, forecast Forecast deliverables preparation, forecast approval, forecast Water level, discharge at all forecast points and flood inundation map (Please bullet 6 issuance (noting that you will need to coordinate in Section 8.9..1), and yearly evaluation report on forecasting performance (Please five FFEWS systems). see Section 8.1) Timing, including latency, pre-processing, runtime, post-processing, forecast preparation, forecast approval, forecast issuance (noting that you will need to
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coordinate five FFEWS systems). These are covered between bullet 1 to bullet 6 in Section 8.9.1 and also see 8.9.2 last para 16 Cost of modelling software : The cost of If HEC-HMS and HEC-RAS are used, they will be free modelling software is listed as USD13,000 per If MIKE is used, DHM has already have Licenses; they will need some additional basin, assuming the same software can be budget to get multiple License and get the latest release of MIKE (release 2016 or used for the five basins, totaling USD65,000. later if already available) What about annual maintenance costs? Should freeware software be used then this price would In case of TUFLOW, Flood Modeller pro or Infoworks ICM or SOBEK, the software will be nil, but there may be extensive coding have to be purchased, and thus USD 65,000 have been kept; we have taken this requirements. The way the cost of modelling price from the WBM home page software is presented does not reflect this.
Because of this, perhaps it should be included as a separate item. 17 Costs: The executive summary should include Included in ES, please see Table 1 to 6 all cost components, ie topo survey, hydromet and rainfall procurement and O&M, modelling software procurement and coding 18 Response time : Section 1 should outline the Have included it with reference in Section 1.5, para 2. response times (eg time of concentration, lagtime) at various locations in the basin. This is important to appreciate the need for rapid forecasts. 19 Data flow : A diagram outlining the data flows Provided, see Figure 14 and supporting texts in Section 8.9.2, bullet 1, 2 and 3 (from observed to harvested by FFEWS to dissemination) would be helpful. 20 Water-level gauge : DHM will take ownership of We have proposed three types of gauges; depending on the site condition and DHM’s
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the gauges. Liaise with DHM on what type of preference, gauge type will be selected at each site. The three types of gauge gauge they will accept, since they might only consideration is also based on specifications given by RTS, the Kathmandu based want downward-looking radar compatible to hydromet equipment provider. Both ADB and DHM advised to contact RTS; DHM their recently installed real time system in many however mentioned us to take note that RTS is only their vendor basins.
21 O&M costs : Outline whether this is per year or Have made them clear in every basin report, see Table 1 to 6 and also please see over a number of years (how many?). Feasibility Report, Chapter 6 and Appendix E 22 Number of hydromet gauges : Ensure reports Have corrected them in each basin report. Sorry that we made some mistakes in indicate internally-consistent numbers of numbers, mainly due to copy and pasting hydromet gauges. The Mohana-Khutiya report is inconsistent. 23 Water-level and discharge budget : Have clarified it, Mentioned in note (c) in Table 3 and Table 16. measurement cost and an O&M cost are separate, what’s the difference between measurement and operation ? 24 Real time database (SCADA): Include the need We will use DHM existing real time database system. to develop a real time data base system We have added a section ( see section 8.9.2) that data from new telemetric gauges integrated to DHM’s existing database. will be transmitted to DHM’s server; however, consultant for this project will do the Updating and maintenance of the database by checking, analysis and quality assurance for these new gauges; we have considered DHM should also described budget for this 25 Flood forecasting approaches : The tabulated Plan map added, please see Figures (maps) 10 to 13 flood forecasting approaches (Tables 12, 13, 14, 15 & 16) are not clear. Plan/schematic diagrams would make this clearer. 26 Dissemination : provide a section on forecast Added, please see Section 8.9, bullet 1 to 5 and also Section 8.9.5 dissemination, ie what, when, how, etc
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27 FFEWS programme : Do not replace gauge-to- Complied; gauge to gauge correlation will be maintained in parallel to other tool gauge forecasting, keep it as an alternative (backup) 28 FFEWS programme : to Figure 8 add/clarify; Clarified, o Gauge installation period o Define what Q1 to Q8 are Regarding model development for five basins, we have considered sufficient resource input (3 international and five national experts. Thus, the staggering of activities will o Rainfall-runoff is a standalone activity remain upto he consultant. The aim in the programme that model for each basin will o Ensure calibration data are available for go in parallel and should be calibrated and validated for all three years, and each calibration activity. There may need to be basin model will be handed over to DHM at the end of 36th month another calibration/validation activity near the end when more data is available. o Ensure all five programmes are not coincident, there should be some stagger in the programmes 29 FFEWS maintenance : programme should During the period of development of this project, which is three years, there will be include at least a 3-year maintenance period. operation and maintenance work and consultant will remain available full time for any Maybe even a 5-year period maintenance support including trouble shooting period. Outline what is required in the
maintenance period; o Pre-season setup (2 weeks) o Early season assistance (2 weeks) o On-call trouble-shooting during flood season o Post-season review (2 weeks)
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30 Coordination of five FFEWS systems : explain These are five different basins and five different models. In our schedule, all five basin how the overall system will coordinate five model will be developed in parallel. We have included sufficient and logical human individual FFEWS systems. resource (input) for this (see Section 8.13). For operation and dissemination, as these will be automated system (existing system of DHM – the new models will be customised to the system), the operation and dissemination time is very minimal, for five models (or 10 models), the run will be made through a batch file. For the first three years, all international and national experts together with DHM experts will remain available. After 3rd year, DHM continues. They have specialists who work on shifts (DHM, 2018)
31 CBRDM: Include CBRDM activities in the West Included. It is in a separate report on CBDRM for all six basins Rapti basin , as this aspect was not fully covered in the World Bank PPCR project. CBRDM should also focus on the role of local municipalities.
32 Flood Shelters: Include Flood Shelters in West Included, please see CBDRM Report, it is a separate report Rapti Basin.
33 Evacuation route: Include the need to prepare Included, please see CBDRM Report; it is a separate report evacuation routes in all the basins based on 2D model results and road network. 34 Flood Risk: Include the provision of flood risk Yes, this is in-built deliverable; flood risk maps will be issued daily, irrespective of a associated with an flood forecast event, so that major event or regular flow; Please see bullet 6 in Section 8.9.1 where we have communities and Government agencies are mentioned flood map as deliverable in each forecast run aware of the risks.
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Table B.2: Comments on FFEWS Reports from WRPPF (Date of receipt: 13/01/2019) Comments from WRPPF were generic for the reports on five basins: Mohana-Khutiya, Mawa-Ratuwa, Lakhadei, Bakraha and East Rapti
Reference Comments from: WRPPF; Received on: Reply consultant 13/01/2019 Bullet 1 The language of the report needs to be We have gone through the report thoroughly, have improved texts and corrected improved edited in standard format as text grammatical error writing in addition to grammatically correct. All Regarding standard format as text writing, the report has been written in Mott the text is to be thoroughly checked. MacDonald’s standard template; so we hope the format is alright Bullet 2 Abbreviated words are to be standardised We have standardised abbreviated words
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Bullet 3 The reports are nice but I found some typing Thanks for your appreciation about the report, we have removed typing errors. errors and some technical issues like installation Number of ground based rain gauge stations have been decided based on research of lot of rain gauge including X-band radar recommendations and based on practice, e.g., in Europe (please see Section 2.2 of installation and water level sensor stations the report for reference). Keeping in mind the future maintenance, the total number of similar to that of UK and Australian standard. stations have been chosen on the higher end of the recommended spatial distribution. Technical discussions are required about 2 For flood forecasting purpose, one station within 10 to 100 km is recommended. Our proposed locations and number of stations. 2 proposed distribution is one in around 100 km . The area is even higher per one gauge in bigger basin like East and West Rapti. Based on the performance of the FFEWS models, which will be developed in this study, we hope that DHM , in future,
can add few more stations bringing the station density like in the UK.
We have apprised the numbers of gauges and their locations to DHM through several meetings; we also sent these documents (Chapter 5 and 6) to DHM; they recommended to decide on the numbers based on hydro-meteorological climate in
Nepal, which we have complied through literature and research reviews. Further to this, we also published two basins reports (FS Report) for M-K and M-R basins in advance in July 2018 showing this spatial distribution of rain gauges, and received feedbacks from ADB and DOI and implemented those in finalising rain gauge numbers in all six basins. Regarding X-Radar rain gauge, we have now dropped this item following ADB;s advice (see ADB’s comment 3 in Table B.1)
Main issue is sustainability of the stations and As of the proposed new stations are automated telemetric stations, they have minimal system for operation and maintenance after the operation cost. The maintenance cost is also minimal, which we believe Nepal project. Government/DHM will continue from their annual budget after this project is completed Proposed water level sensors like floating in stilling well and pressure air bubble sensors are We have proposed three types: sensor in stilling well, air bubbles sensors and radar not operable in high sediment load river. sensor; deepening on site condition and DHM’s preference, the gauge type will be The hydro-metric stations may need special selected during procurement and installation. structures like flood pillar in river channel having high fluctuations of water levels and change of river channels as well. The budget includes all infrastructures for installation of a gauge, rain gauge or water level gauge. We have mentioned this in the report (please see Section 5.5, para 1) as follows: the budget includes procurement, installation, testing, calibration, monitoring, The detail design of cableway is not included and operation and maintenance for 3 years which vary with proposed site to site of the 383877 | REP | 0038 | 4 April 2019 Flood Forecasting andstations. Early Warning estimated System: Lakhandei cost of Basincableway is same for all basins although there are variation in We have provided all information needed for installation of the cableway (see Figure proposed number of stations with discharge 7); the equipment technician would have to deliver this; more-over, DHM has already measurement cableway in Mohana and West Rapti basin; so a similar one should be constructed. Mott MacDonald | WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal 89 Flood Forecasting and Early Warning System: Lakhandei Basin
4 Regarding hydrological modelling and flood Chapter 8, in Table 19 to 23, specific reach-wise models have been proposed.. early warning system development, list of Modelling cost: models are provided but not recommended finally although they have estimated cost for The hydrological and hydraulic models for each basin are separate model; i.e., software purchase. The development cost of the separate model set-up preparation, separate calibration and validation; thus, the cost hydrologic and hydraulic model shall reduce is almost similar among the basins; however, as expert will become experienced significantly once it is built for a basin and just through work in one basin, the cost will slightly reduce in other basins, and we have replicate with other with small changes based considered this factor, please see texts explaining this in Section 8.13 on data availability.
All civil works and fencing are inclusive in the cost; please see Section 5.5, para 1. We The cost of land acquisition and fencing and assume that all rain gauges will be installed at Government/Public premises, e.g., civil works etc. not mentioned. DHM Office premise, DWIDM office premise, Local municipality premise, Administration Office premises, Gram Panchayat etc.
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