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International Journal of Disaster Risk Reduction ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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International Journal of Disaster Risk Reduction

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Community responses to flood early warning system: Case studyin Kaijuri Union, Bangladesh

S.H.M. Fakhruddin a,n, Akiyuki Kawasaki b, Mukand S. Babel a

a Water Engineering and Management (WEM), School of Engineering and Technology (SET), Asian Institute of Technology (AIT), Thailandb Department of Civil Engineering, The University of Tokyo, Japan

a r t i c l e i n f o

Article history:Received 8 May 2015Received in revised form6 August 2015Accepted 22 August 2015

Keywords:Medium-range flood forecastsAgriculture riskEarly warningCommunity response.

x.doi.org/10.1016/j.ijdrr.2015.08.00409/& 2015 Elsevier Ltd. All rights reserved.

esponding author.ail address: [email protected] (S.H.M. Fakh

e cite this article as: S.H.M. Fakhrudladesh, International Journal of Disa

a b s t r a c t

Early warning is a key element of disaster risk reduction. In recent decades, there have been majoradvancements in medium-range and seasonal forecasting. Babel et al. (2013) [1] developed an experi-mental medium-range (1–10 days) probabilistic flood-forecasting model for Bangladesh. This progressprovides a great opportunity to improve flood warnings, and therefore, reduce vulnerability to disasters.This paper describes an integrated system on medium-range flood forecasts based on agricultural users'needs in order to reduce the farming community's flood impacts. For example, 1- to 10-day forecasts mayprovide farmers a range of decision options such as changing cropping patterns or planting times. Themethodology included risk and vulnerability assessments conducted through community consultation.The study involved developing a flood risk map and response options to flood risk probabilistic forecastsbased on farmers' needs for early warning. Understanding the use of probabilistic forecasts is still verylimited, and operational forecasters are often skeptical about the ability of forecast recipients to un-derstand the ensembles prediction system. This study showcases the ability to use probabilistic forecastsfor operational decision-making purposes. The forecast lead-time requirement, impacts, and manage-ment options for crops and livestock were identified through focus group discussions, informal inter-views, and surveys. The results included flood risk mapping according to the vulnerability of the com-munities in the study area and the early warning impacts during and after the flood events.

& 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Flood risk is widely considered to be the product of threecomponents: hazard, vulnerability, and exposure (value) [8]. In itssimplest structure, flood risk is computed by multiplying thesethree components [30]. However, in practice, vulnerability andexposure (value) are usually combined into a single vulnerabilityfactor, and flood risk is computed using hazard and vulnerabilitycomponents [27]. Flood risk is the measure of the seriousness of aflood hazard and its probability. It is the probability of an eventmultiplied by its consequences (for example, estimated damages)[33,34,40,49]. Flood risk is also defined as the hazard multiplied byvalues and vulnerabilities in which “hazard” is the threateningnatural event (including its probability of occurrence), while va-lues (or values at risk) are items like buildings, or people that re-side at the location [28]. Vulnerability is the lack of resistance todamaging or destructive forces [4,29]. However, natural hazardsusually do not manifest themselves in one single event with a

ruddin).

din, et al., Community resposter Risk Reduction (2015),

given probability of occurrence, but rather in many different formswith an almost infinite number of variations [28].

Farmers, for instance, face a number of interconnected risksregarding crop production, such as fluctuating prices and markets,regulations, technology, and finance [45]. Crop production or yieldrisk occurs because agriculture is affected by many uncontrollableweather-related events, including excessive or insufficient rainfall,extreme temperatures, hail, and other factors like insects anddiseases [36]. A long-lead early warning for natural disasters couldreduce this risk for farmers and help them sustain the quality andquantity of agro-based products [16]. Climate forecast applicationprojects in pilot areas in Bangladesh demonstrated how farmerscould better utilize long-lead climate information to plan theirplanting and harvesting [41, 15].

Flood forecasting has received increased attention in recentyears [3,5]. Advances in meteorological, hydrological, and en-gineering sciences are fast generating a range of new methodol-ogies for forecasting weather and flood events, including ensembleprediction systems (EPS) and new hydrodynamic models [14, 12].The risk of more intense and frequent events, along with increasedvulnerability to such events, underscores the need to protect the

nses to flood early warning system: Case study in Kaijuri Union,http://dx.doi.org/10.1016/j.ijdrr.2015.08.004i

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S.H.M. Fakhruddin et al. / International Journal of Disaster Risk Reduction ∎ (∎∎∎∎) ∎∎∎–∎∎∎2

community from greater social and economic damage. Thus, earlywarning of such catastrophic events could enhance disastermanagement planning and reduce the potential consequences atthe community level, as well as at the national level.

For a flood-prone country like Bangladesh, flood forecastingtechnology plays a crucial role in saving lives and property. TheFlood Forecasting and Warning Center (FFWC) of the BangladeshWater Development Board (BWDB) is responsible for flood fore-casting within Bangladesh [43]. The flood forecasting models usedby the FFWC are based on MIKE 11, a one-dimensional modelingsoftware used to simulate water levels and discharges in rivers,producing up to 48- to 72-h deterministic forecasts. The experi-mental model produces 1- to 10-day probabilistic discharge fore-casts. Due to its probabilistic nature, it has many limitations at theuser level for interpretation, translation, and understanding of theearly warning message. Although there are many disaster riskmanagement initiatives implemented by different INGOs andNGOs, research and capacity building activities on the applicationof medium-range probabilistic flood forecasts is limited. For ex-ample, the Center for Environmental and Geographical Informa-tion Service (CEGIS) is piloting flood early warning systems at thecommunity level, using existing 48-hour deterministic forecasts[41]. However, a 2-day lead-time could only be used for emer-gency evacuation and household preparation work.

To enhance the lead-time of flood forecasts, the ensembleprobabilistic flood forecasting technique is applied [9,38]. Opera-tional medium-range flood forecasting systems are increasinglyadopting an ensemble of numerical weather predictions (NWP),known as ensemble prediction systems [21,22,39,47,6]. The ob-jective of this paper is to develop a flood risk map based on en-semble forecasts of 1–10 days and design user response optionsfor decision-making. Understanding the use of probabilistic fore-casts is still very limited and operational forecasters are oftenskeptical about the ability of forecast recipients to understand anEPS [11]. This study showcases the ability to use the probabilisticforecasts for operational decision-making purposes.

Fig. 1. The Brahmapu

Please cite this article as: S.H.M. Fakhruddin, et al., Community respoBangladesh, International Journal of Disaster Risk Reduction (2015),

The medium-range ensemble forecasts are not common inmany countries and have never been applied at the communitylevel. This research therefore focused on developing a flood riskmap based on these ensemble forecasts of 1–10 days, designinguser response options after community consultations.

2. Study area

The Brahmaputra is a major transnational river, covering adrainage area of 580,000 km2, of which 50% is in China, 34% inIndia, and 8% in Bhutan and Bangladesh [7,19]. Accurate andtimely forecasts of river flow have the potential to provide criticalinformation for agriculture risk management and disaster miti-gation. Nowhere is the need for such forecasts more urgent than inthe Bangladesh delta that lies at the confluence of two of thelargest river systems in the world: the Brahmaputra and theGanges. Combined, these rivers can reach discharges of well over100,000 m3 s�1. The river starts rising in March/April due tosnowmelt in the Himalayas, and the high water level period beginsin June. The water level continues rising until reaching its annualpeak, which lasts from late July until mid-August.

Fig. 1 shows the Brahmaputra River Basin and the pilot sites inthe box. In Bangladesh, crops are highly vulnerable to the hydro-logical cycle. During the monsoon when the water level in theriver and flood plain starts rising, three different crops are in threedifferent stages of growth. Fig. 2 describes the relationship be-tween crop calendars and hydrology in Bangladesh. When theboro (rice) crop is at the harvesting stage, an early flood couldseverely damage the crops. Consequently if the transplanted aman(rice) are in the seed bed they could be washed away. Advancedflood forecast information could assist farmers to take appropriateaction. The study area chosen is Kaijuri Union of Sirajganj, which isflooded by the Jamuna River (the main distributary channel of theBrahmaputra River) almost every year.

tra River Basin.





Fig. 2. Relationship between hydrology and cropping calendar (H-harvest, S-sowing) (prepared based on 2000–2010 year rainfall and water level data from the pilot area).

S.H.M. Fakhruddin et al. / International Journal of Disaster Risk Reduction ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 3

3. Methodology

3.1. Medium-range flood forecasting model

The medium-range flood forecasting model discussed in thispaper is described in Ref. [1]. The lumped and semi-distributedhydrological model were used in multi-model simulations to pro-vide discharge forecasts of 1–10 days at two major locations, theBahadurabad station on the Brahmaputra River and at HardingBridge on the Ganges River (Fig. 2). The aim of the multi-modelapproach applied here is to minimize hydrological modeling errors,with total hydrologic forecast uncertainty. The semi-distributedmodel employed here is a simple nine-parameter two-layer re-presentative area soil model parameterizing most of the physicalprocesses of water storage and transport with a slow and a fast time-scale response. Water balance calculations are carried out on a0.50�0.50 computational grid, with each grid treated as a sub-catchment. At this scale, heterogeneous water storage and transportprocesses are homogenized, with the model providing only con-ceptual representations of these processes. The multi-model ap-proach is automatically recalibrated daily utilizing both the updatedlumped and distributed hydrologic model hindcasts by rankingmodels based on their total hindcast residual error (the squareddifference between the modeled and observed discharges). A mul-tiple linear regression of both hindcast time series against the

Fig. 3. Methodology for fl


observed discharge time series is performed to minimize multi-model residual errors and to evaluate the resultant residuals of thehighest-ranked model and the multi-model using the BIC to avoidover-calibration. If the multi-model BIC is minimized, then the re-sultant regression coefficients are used to generate “multi-model”forecasts and hindcasts; otherwise, the highest-ranked model can beused. The 1–10 days forecasts were generated, and they supplied 51sets of ensemble forecasts for a particular day at each dischargeprediction point.

3.2. Flood risk mapping

The overall methodology of the research is described in Fig. 3. Themethodology for developing a flood risk map based on the medium-range forecasts was designed in two major steps: (a) initial dataprocessing and setting up the hydrological model for predicting therunoff, probability analysis, and development of a flood risk map;(b) interviews, focus group discussions (FGD) with farmers, andworkshops to determine vulnerability and risk based on communityperceptions and historical records. The modeling technique employsmultiple linear regressions of observed discharge and forecastedprecipitation along with a non-linear “effective rainfall” filter basedon the idea of linear storage reservoirs and a model structure similarto the Unit Hydrograph Theory's model. The details of this hydro-logical modeling technique have been published in Ref. [1, 26].

ood risk assessment.





Fig. 4. Risk assessment frameworks (modified from [23]).

Fig. 5. Flood risk map of 5- and 10-days forecasts based on probabilistic hydrological models and community consultations.


3.3. Community response

This paper discusses the second step, which involved thecommunity interviews and response options for flood risk prob-abilistic forecasts. Based on 10-day flow forecasts and dischargeensemble thresholds, the impact outlook has been designed usingrisk assessment frameworks (Fig. 4) developed by Ref. [23]. Thevulnerability and risk assessment is one of the major tasks forassessing flood risk. Vulnerability is the lack of resistance to da-maging or destructive forces [30,42]. If the values of hazard andvulnerability are combined to form the value C, denoting theconsequences resulting from a single event with the probability P,then the risk R – from only one event – can be written as R¼CxP.The vulnerability and risk assessment in this research follows theframework of characterizing and managing risk. Risk assessmentincludes risk scoping (i.e. established target and criteria through


consultation with stakeholders), characterization of risk, the eva-luation process, risk management, and feedback from the com-munity on risk perception as shown in Fig. 4.

A qualitative research strand was selected for this research as itenabled investigation of the phenomenon in a natural setting andattempted to make sense of, or interpret, those views by con-sidering the meanings people brought to them [10]. Detailed as-sessments in the community helped to define the scope, char-acterize, and evaluate the risk through three FGDs in the agri-culture sector. In addition, individual household surveys with 20farming families were conducted. Discussions with local-leveldisaster management committees and informal interviews withgovernment officials (i.e. Department of Agriculture, Fisheries, etc.)and local NGOs (i.e. Gono Kalyan Sangstha (GKS), BRAC) wereconducted. In risk scoping, the target population was definedthrough community consultation. The flood event and its related





TaEx


risks were assessed, and the coping methods of the communitywere identified. Other factors identified by FGD were the forecastlead-time requirements, users' needs, impacts, and managementoptions for crops and livestock to customize forecast products.Households were asked about how they received flood forecastinformation, their level of understanding, and whether they tookany action based on the forecast, among other questions.

4. Results

4.1. Flood risk maps

Flood risk maps were drawn based on the flood thresholdsidentified by users in the FGD. The user-identified thresholds werethen verified with level surveys based on local benchmarks. Theflood risk classes have been categorised as very low (17 m), low(18.5 m), medium (18.6 m), high (19.5 m), and very high (19.6 m).The average elevation of Sirajgonj is 13 m.sl. The risk map wasthen produced by integrating the SRTM 90 m resolution digitalelevation model (DEM) and land-use GIS dataset. Discussions withthe community were held to identify different flood levels andthreats based on historical floods. A level survey measured thesethresholds from the sea level. Based on the historical dataset, floodfrequency was evaluated to match the thresholds and put theminto the model. From the hydrological model results, the authorscalculated the mean ensemble forecasts for 1–10 days to definethresholds. Fig. 5 shows the flood risk map based on 10-daysforecasts, reflecting the results of consultations.

4.2. Community-based early warning system

In the study area, people receive early warnings regardingrainfall and floods through radio and television, which is deliveredby the Bangladesh Meteorological Department (BMD); however,the acceptability is very low (Table 1). Rather than act on thegovernment's early warning, people prefer to rely on their ownexperiences regarding rainfall and floods in some areas. For in-stance, people in Kaijuri Union feel confident in their own as-sessments of near-future rainfall and flood conditions by observingthe wind direction and clouds. Since they do not completely trust

ble 1isting early warning communication system in the study area.

Source Kaijuri Union

Media Acceptability

Own experience (observing wind directionand cloud condition)

Deliver bythem

High

Government TV/radio Low

Table 2Possible agriculture activities based on different lead times of forecasts.

Agriculture activities based on different lead time forecast

2-day lead time 10-day lead time 20

– Harvest matured cropsas much as possible

– Not effective in mostcases

– Not effective if cropsare in growing stages

– Store household goods– Good for in-house

preparation

– Start to harvest ripening crops if situation demands– Stock seeds for emergency period– Delay seed bed preparation– Abstain from planting crops– Union Disaster Management Committee (UDMC) could

warn people through announcements, posters anddrumming regarding forthcoming flood

–

–

–


early warning messages disseminated through television andradio, they may not take sufficient preparedness actions, whichthen leads to flood damage.

4.3. Lead time for early warning

The study area is situated in the riparian zones of the Brah-maputra River. Unfortunately, these areas experience negativeimpacts due to the irregular and ill-timed riverine floods. Thestudy found that existing flood forecasting and warning systemshave been ineffective. Cultivators often lose crops each year, whichcould have been at least partly harvested if farmers had received,and then acted upon, timely warning information about comingfloods. A survey was conducted where four options (2-day, 10-day,20-day, or 2- to 3-month lead times) were offered to assess themost suitable lead time for early warning information for farmersin the study area.

According to the results, a 2-day lead time for early warningwould be too narrow a period to be able to harvest crops and, inmost cases, not effective. A 10-day lead-time would be more ef-fective to prepare for harvesting crops, irrespective of whetherthey are ripening or already matured. With this lead-time, culti-vators can also store seeds for emergency seedbeds. A 20-day lead-time was suggested as the best possible option, but operationallythis will not be feasible due to the level of uncertainty in theweather forecasts. The survey revealed that most of the partici-pants preferred a 10-day lead time as the most suitable for de-veloping a flood EWS. Table 2 summarizes the results of interviewswith the farmers on possible agriculture activities based on fore-casts with different lead times.

Similar findings for fisheries and households were also col-lected. Most of the households explained that they use forecastinformation to save their household assets and livestock, storefood, and move to a safe place, since the lead-time is very short.People suggested that they need forecasts with longer lead times:for agriculture, 10–20 days for early harvesting or crop systemselection; for fisheries, 5–7 days for protecting fishing nets; andfor households, 5–7 days for advanced preparation.

4.4. Early warning dissemination

During community consultations, people also discussed thesources, timing, dissemination place, and media of an earlywarning system. Most people preferred local announcements asthe media for dissemination, and the source of messages could beschool teachers, religious leaders (Imam), or the Union Parishad(lowest administrative unit) members who would inform peoplein schools, colleges, mosques, or bazaars. People would prefer lo-cally available and easily understood early warning information.Most of the people suggested megaphones, TV, radio, and mobile

-day lead time 2- to 3-month lead time

Steady starting of crop harvestingUDMC could hold meetings with the head ofPara (segment of village) regarding preparationto rescue the assets and life from floodArrangement of seed bed on high land

– Early yielding varieties ofcrops could be cultivated asalternative options





Table 3Dissemination tools for early warning system (Based on questionnaire survey).

Media Land phone Fax Mobile Wireless Radio TV Mike Inter-personal Billboard Poster

Rank 4 6 1 3 5 2Availability N N Y N Y Y Y Y Y YAcceptability Y Y Y

(1¼More preferred, 5¼ less preferred, Y¼Yes, N¼No).


phone technologies for information dissemination (Table 3). An-other study [25] found that television was widely used regardlessof income level. However, lower-income respondents tended toalso utilize lower-technology modes, such as radios and loud-speakers, in contrast to the Internet-based modes used by higher-income respondents.

Floods affect the people and livelihoods of the pilot study areaevery year. According to estimates from local government offices,recent floods in the respective areas have caused damage toagriculture, fisheries, homesteads, and infrastructure.

To overcome their losses, people undertake several prepared-ness, response, and recovery measures for each sector. They aremore interested in preparedness activities that will reduce theirlosses and provide permanent solutions, such as embankments ormore stable structures.

4.5. Present community-based adaptation mechanisms

For flood risk management, communities more or less dependon their indigenous knowledge. Decision-making based on med-ium-range forecasts was not a practice in any area. In discussionswith farming communities, farmers said they are keen to receiveprobabilistic information so they could verify their perceptionswith scientific information. The people within the study areas havevast experience in coping with frequent and irregular floodingevents. Based on discussions and community consultations, theirpresent coping strategies in different sectors have been dividedinto pre-, during, and post-flood conditions as described belowand in Table 4.

4.5.1. Pre-flood coping strategiesFor instance, in preparation for floods, cultivators stock cereal,

dry food, seeds, and fodder. They also carry cow dung to higherland and sow broadcast aman (rice). To mitigate the impact offloods, they develop seedbeds on flood-free land, cultivate seed-beds and vegetable plantations on floating piles of water hyacinth,construct vela (rafts) made of banana trunks, and cultivate vege-tables in tubs. To ensure sufficient clean water, people arrangereservoirs for storing drinking water. They also stock water-pur-ifying medicine such as potash alum and tablets and conserverainwater. Households stock bamboo, rope, jute carpets, bamboofences, and other materials for constructing flood time latrines.They also stock materials for constructing temporary bridges forimproved small-scale transportation during floods, as well as ar-ranging boats and vela for larger-scale transportation andcommunication.

4.5.2. Coping strategies during floodsDuring floods, they usually fetch drinking water from distant

places due to damage to tube wells. They make temporary latrinesusing bamboo, rope, and jute carpets/bamboo fences where theyare taking shelter. They also use vela made of banana trunks andboats.

4.5.3. Post-flood coping strategiesImmediately after the floods recede, the cultivators conduct


activities such as rapid development of seedbeds, clearing hya-cinths and weeds from the fields, preparing croplands, and buyingpaddy saplings from distant places to partially recover losses. Theyalso clean their homes and repair tube wells and other drinkingwater sources. They dismantle the temporary latrines and repairthe regular latrines. Temporary bridges are dismantled and roadsare repaired with the help of the Union Parishad (lowest admin-istrative unit).

4.6. Flood impact and response outlook

An impact outlook or response outlook matrix has been de-veloped based on discussions with farmers and other stakeholdersat the community level. One aim is to link the matrix with thehydrological model and community members' indigenousknowledge and practices for decision making. Different types offloods cause different damages, so farmers need to plan accord-ingly. Table 5 presents the disasters, impacts, and managementplan matrices for the agriculture sector. This paper focuses on twotypes of flood in the study (e.g. early flood and high flood). Thecommunity suggested that these response options, together withearly warning information, would help them in quick decisionmaking and to better understand the early warning information.

5. Discussion

The paper focuses on flood early warning forecasting and re-ducing the impacts of future flood events. Disaster preparednessand early warning are important to avert catastrophes [20], al-though communicating risk in an easily understandable mannerwith the community is often neglected. The quantification offorecasting risk is often not utilized or understood by the com-munity [37]. However, past research has also documented howmuch even experts, including meteorologists [35, 13] sometimesstruggle to understand probabilities and probabilistic forecastscorrectly [46].

In any case, it is important to build farmers' capacity to un-derstand probabilistic flood information. For example, let us as-sume that a certain crop is 80% mature and will be nearly ripe byearly July. It requires another ten days to reach complete maturity,sometime around mid-July. Having received a warning, the farm-er's decision to be taken in early July is whether to harvest thecrop before its complete maturity or wait until the middle of Julyto ensure good quality. If the forecast indicates a higher chance ofabove- average flow in mid-July that may inundate paddy fields,the harvest would be affected substantially. Inundation during theharvesting stage may lead to a complete loss of the product andthe entire cost of investment. As the community mentioned duringthe consultations, they usually make decisions each season basedon their indigenous knowledge or previous experience. Probabil-istic flood forecasts could help them to verify their traditionalknowledge and apply it appropriately.

This study's findings show that current ensemble weatherpredictions can enable skillful detection of hazardous events withforecast horizons as long as 5–10 days in the Brahmaputra river






basins, provided that the initial conditions are estimated correctly.This depends on the skill of input weather forecasts and on thedelay between the meteorological forcing and the hydrologicalresponse in the river basin.

As countries move forward with developing an effectivewarning system that utilizes ensemble flood forecasting capacity,there are several major challenges

� Communicating data uncertainty to professionals down the linewho are responsible for flood forecasting, warning, and

Table 4Existing adaptations for flood.

Affected sector Pre-flood During flood

Agricultural Fallow land (left for a period unused) Carry cow dung to higHarvest of green grain FallowCultivation of early verities Harvest immature cropCultivation of Deep Water rice (DWR) Preserve agricultural eStock of dry food, cereal, seed andstraw

Seed bed and vegetablwater hyacinth and ‘Ve

Prepared Seed bed at higher places Seed bed on safe landStock cereal, dry food (flat rice, friedrice)

Take Loan from NGO

Stock seed & straw Vegetable on tob

B. Aman cultivation Carry cow dung to higEmergency IRRI crop storage.

Fisheries Arrange loan for buying gears andcrafts

Sell

Catch fish with vashal net Use nets to prevent fis

Fish consume/sell to the market Buy gears and crafts; ethe pond with net/banfish and delay release

Install pipe in the pond dyke to bal-ance water

Due to high water leve

Prepare net for encircling the culturedpondRepair & heighten cultured pondboundaryRepair boat and net before flood

Livestock and poultry No pre activities Sale with low priceConstruction of temporary highplatform

Use of water hyacinth

Nothing to do Eat for family purposePlinth level rise of cattle shed Make sub-platform; ar

nearby relative's houseprotecting diseases.

Sell to market Rise the plinth of farmbankment /School/Mad

Stockpiling of fodder Shifted to higher placeVaccination their livestock

Household andHomestead

Preserved fuel wood for emergencyperiod

Prevent base washout

Heighten the house base as well asthe homestead premise so that re-main flood free.

Take shelter in the roa

Preparing of mud oven Try to protect flood waStock fire box and wax Arrange boat for shiftin

Parishad

Temporary platform construction Prevent base washoutTie up idle home appliances Shift of home applianc

embankment/closer schouse)Try to stay in his/her oUprooted sapling of ecpost-flood planting


emergency response [17]. More research is needed on howflood forecasters and emergency service responders mightutilize such uncertainty information.

� Achieving the timely dissemination of forecast and warninginformation to relevant stakeholders and communities [2,48].

� Changing warning response behaviors. The main purpose offlood forecasting and warning is to change people's behaviors toincrease adaptation, save lives, and reduce damage [31,32].However, people do not respond to warnings unless they whollytrust them [24,44]. Though it is well known that false alarms

Post-flood

her land Clearance of hyacinth & weedsCultivation of irri rice

s Loan from NGOquipments and seed in high platform No tillage crop (mustard, groundnut)es plantation on the floating pile ofla’ made of banana trunks

Cropland preparation

Cultivate short time vegetable.Rapid seed bed preparation

Sapling (especially of T. aman) to buyfrom distant places

her land

Catch fish after water recession

h loss Fish fry buy from the market and releaseto the pond

xtensive open water fishing; encirclena to prevent fish escaping; harvestof fry, fish sell to market.

Pond preparation for seed release/repairthe pond dyke

l catches fish nearer side of house Lime for protecting disease

Repay the loan

Supply fish seed through Upazila fish-eries extension officerTake loan from fishery department andNGO

Get back to the own placeto prevent mud formation Repair the farm house

Sellrange safer shelter (on embankment/) and sell in low price, vaccination for

Treatment and medication continues

house; arrange safer shelter (on em-rasha nearby relative's house)

Arrange medication if get sick

, relatives house or embankment Vaccination for protecting diseasesGet back to the own place

using water hyacinth and banana tree Cleansing of house and homesteadpremise

d, embankment or any high place Reconstruction

ter with sand bag Cultivate short duration vegetableg household assets through Union Heal up the broken places with soil or

sand; cleansing of house and homesteadpremise.

using banana tree Loanes and furniture to safer place (onhool/Madrasha/nearby relative's

Repair the destroyed stuffs

wn house Sale trees, vegetable etc. with low priceonomic importance and conserve for Take loan from NGO





Table 5Disaster, impact and strategy options (developed for two flood scenarios).

Disasters Crop Stages Season/month

Impacts Time of floodforecast

Alternative managementplan

Earlyflood

Seedling and ve-getative stage

Kharif II-June–July

Damage seedlings damage earlyplanted T. Aman

Early June Delayed seedling raising.Gapfilling, skipping

Harvesting Kharif IJune–July

Damage to the matured crop Early June Advance harvest

Near maturity June–July Yield lossPoor Quality May end Early Harvest

Harvesting June–July Damage yield lossPoor Quality March–April Pot culture (homestead) useresistant variety

Highflood

Tillering Kharif IIJuly–August

Total crop damage Early June Late varieties direct seedinglate planting


deteriorate compliance, the so-called cry wolf syndrome, but itis essential to give them understanding on how to relay onprobabilistic information and deal with uncertainty. If peoplecould understand the actual reason of false alarm the effectcould be minimized.

Research indicates that people's images of the future areshaped by their experiences of the past. A major constraint on theability to use flood warnings is reliance upon individual


experience rather than on information received from officialsources [12,18]. Nonetheless, little is known about how thesepsychological and sociological factors combine with warnings topredict who may be at maximum risk for negative outcomes. Suchstudies are critically needed so that more effective warning mes-sages can be designed. An integrated system including scientificand social information would enable users to interpret andtranslate scientific knowledge into user-friendly language for re-sponses at the community level.






6. Conclusion

Flood-warning systems need to be seen as providing(a) forecasts of floods, (b) warning bulletins to those at risk, and(c) a communication mechanism for disseminating warning mes-sages to those who need the information as a basis for their re-sponse. This “whole-system” approach needs to be integrated tomake the warning system successful. The ensemble probabilisticearly warning of an impending flood is useful and could help thecommunity interpret and translate scientific information into riskinformation. The increased understanding of probabilistic long-lead flood forecasting is valuable for society and for the protectionof agriculture in flood-prone areas.

Even though forecasts include a level of uncertainty and may bedifficult to apply, there is a strong potential to use medium-rangeforecasts for strategic decision making and planning, as identifiedthrough community consultations. Ten-day forecast information, to-gether with the response options, can assist communities in design-ing disaster management plans and reducing their disaster risk.

Reference

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[3] G. Blöschl, Advances in Flood Forecasting and Implications for Risk Manage-ment. Institute of Hydraulics and Water Resources Engineering, TechnicalUniversity of Vienna, 2010.

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http://refhub.elsevier.com/S2212-4209(15)30050-9/sbref1
























http://dx.doi.org/10.1002/met.194

























http://www.southasiafloods.org/Document/ffb/index.html




http://dx.doi.org/10.1002/asl.270



http://dx.doi.org/10.1175/2009JHM1006.1



http://www.wamis.org/agm/meetings/worisk06/S2-Hay_WORISK.pdf

http://www.wamis.org/agm/meetings/worisk06/S2-Hay_WORISK.pdf



http://dx.doi.org/10.1111/jfr3.12144

































http://dx.doi.org/10.5194/hess-16-3127-2012











http://dx.doi.org/10.1017/S1355770X08004828

http://dx.doi.org/10.1017/S1355770X08004828

http://dx.doi.org/10.1017/S1355770X08004828

http://dx.doi.org/10.1017/S1355770X08004828





















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