9 New urban monitoring networks with low-cost sensors can measure hyper-local floods in real- 10 time in hundreds of locations. These novel networks promise enhanced flood risk management, 11 especially within cities where floods can be extremely local. However, current sensor deployment 12 strategiesrelyonlimitedmetrics(e.g.,proximitytodenselypopulatedareas)anddonotadequately 13 account for the various potential monitoring uses and stakeholders (e.g., emergency responders, 14 critical infrastructure managers, and researchers). Thus, cities have no methodological framework 15 tocomparetheholisticbenefitsofdeployingnewhyper-localsensorsindifferentareas. Toaddress 16 thisgap,wedevelopaframeworktoprioritizeurbanareasforsensordeploymentbasedonpotential 17 usesforenhancedfloodriskmanagementandtheexposureofinfrastructureandcommunitytohigh 18 flood hazards at micro-urban scales. This framework includes (1) obtaining stakeholder feedback 19 on the potential uses of sensors and relevant metrics for decision-making on their deployment, 20 (2) quantifying these metrics with publicly available data to integrate them with flood hazard 21 information through probabilistic risk analysis, and (3) combining the metrics to identify areas to 22 beprioritizedforsensordeployment. WetestedtheframeworkwithacasestudyinNewYorkCity, 23 1 Negri, May 15, 2024 adenselypopulatedurbanareawithhighlyheterogeneouscommunitiesandinfrastructureexposed 24 to high flood hazards. Through elicitation with 45 local stakeholders, we identified 32 potential 25 uses and 58 metrics to prioritize areas for sensor deployment covering flood risk management, 26 the welfare of residents, and the protection of critical infrastructure (e.g., transportation, drainage, 27 and energy). Overall, the proposed framework and case study offer new insights into how modern 28 monitoring networks can help to enhance flood disaster risk management in cities. 29 INTRODUCTION 30 Rapid urbanization and the changing climate have exacerbated flooding for many cities (Dav- 31 enport et al. 2021). For example, in 2021 alone, New York City experienced two unprecedented 32 floodingeventsthatparalyzedthecity,inundatingnumeroussubwaystationsandkilling13people 33 (Plumer 2021; Newman 2021): on September 1st, Hurricane Ida delivered 3.15 inches of rain 34 withinanhour,surpassingthepriorrecordof1.94inchessetbyTropicalStormHenrionlytendays 35 before. 36 Flood monitoring is crucial for flood risk mapping, flood model validation, and flood damage 37 assessment activities in cities (School 2018; Sarchani et al. 2020; Chen et al. 2021). Traditional 38 approachesforrecordingfloodhazardsincludestreamgaugesandfieldinspectionsforwatermarks 39 (Sarchani et al. 2020). However, these methods lack the scalability and accuracy to monitor urban 40 floods effectively. For example, stream gauges are designed for use along river lines rather than 41 inland areas within cities (Krabbenhoft et al. 2022). Watermarks indicate maximum flood depths 42 and can sometimes be taken within cities, but the locations where such (often inaccurate) marks 43 are preserved are extremely limited (Gardner et al. 2023). 44 Modern techniques for flood monitoring can significantly improve the coverage of urban flood 45 measurements. For instance, remote sensing networks, which use satellite sensors, can provide 46 measurements for entire cities with a resolution of less than 10 meters (Chawla et al. 2020). Due 47 to their large coverage, these networks have been successfully used for monitoring hydrologic 48 parameters in coastal cities and evaluating the impact of urbanization on flood risk (Bhatt and 49 Srinivasa Rao2018; Munawar etal. 2022). However,the high spatialresolution of thesenetworks 50 2 Negri, May 15, 2024 isoffsetbyarelativelylowtemporalresolutionofapproximatelyonedayinthebestcases(Chawla 51 et al. 2020), which precludes them from capturing severe, short-duration floods that often occur 52 in cities (Alipour et al. 2020). In addition, the accuracy of remote sensing in dense cities can 53 be affected by buildings that obstruct satellite measurements of the ground (Mason et al. 2012; 54 Giustarini et al. 2013). 55 An emerging flood monitoring technique offers opportunities to address the shortcomings of 56 remote sensing in cities. These networks consist of multiple low-cost sensors installed throughout 57 citystreetsandsidewalkstomeasurefloodsinreal-timeatlargeurbanscales(Figure1). Examplesof 58 such“urbanfloodmonitoringnetworks”includeFloodNetinNewYorkCity(NYC)andStormSense 59 in Hampton Roads, Virginia (Loftis et al. 2018; Silverman et al. 2022; Mydlarz et al. 2024). 60 FloodNetpresentlyoperates ∼85sensorsinNYC;thisnumberwillreach500by2027aspartofa 61 $7.2millionprojectfundedbythecitytoincreaseclimateresilience(Waraich2023). Thesesensors 62 measure water depth with ±5mm precision at one-minute intervals. During Hurricanes Henri and 63 Ida in 2021, the sensors recorded remarkably rapid changes in water depths of almost three feet in 64 less than an hour; see Figure 1 (FloodNet 2024). However, urban flood monitoring networks are 65 limited in spatial coverage compared to remote sensing, as floods are only measured at locations 66 wheresensorsaredeployed. Thus,theyrequirecitiestodevelopastrategyfordeploying(alimited 67 set of) sensors that maximize network effectiveness in flood risk management. 68 Methodstoevaluatesensorplacementforcollectingdataonnaturalhazardshaveeitherfocused 69 on a single criterion such as hazard intensity (Krause et al. 2008; Wu et al. 2012; Du et al. 70 2014; Chang et al. 2019; Yu et al. 2022) or adopted multiple metrics that capture a broader 71 representation of risk, like proximity to critical infrastructure and social vulnerability (Sun et al. 72 2018;Sunetal.2019;Tienetal.2023). Thesemethodshavetwomainlimitations: (1)theyfailto 73 account for multiple potential users and uses of a monitoring network (e.g., government agencies 74 formonitoringcriticalinfrastructure,researchersforimprovingknowledgeofhyper-localhazards, 75 thegeneralpublicforreceivingreal-timehazardalerts,etc.);and(2)theiranalysisisnotexplicitly 76 informed by formal risk quantification, which requires combining spatial information on hazard 77 3 Negri, May 15, 2024 Fig.1.AdepictionofatypicalFloodNetsensor(left)alongsidesampledata(right)demonstrating typical sensor performance during a flooding event. The lead author took the sensor picture from NYC streets. The picture does not imply that NYC officers or the FloodNet project endorse this study’s results. with metrics related to exposure and vulnerability. 78 We propose a framework for decision-making on low-cost sensor deployment in urban flood 79 monitoring networks that addresses these two gaps. First, our framework identifies potential 80 stakeholders of the monitoring network, to determine a comprehensive list of its various uses and 81 establishacorrespondingsetofsuitablemetricsforevaluatingpotentialsensor locations. Second, 82 theframeworkintegratesthesemetricswithfloodhazardinformationinaprobabilisticriskanalysis 83 todeterminethelikelihoodandseverityoffloodimpacts(e.g.,expectedannualnumberofflooded 84 hospitals) across different urban areas. The result is a novel risk-informed, stakeholder-oriented 85 spatial mapping of areas that should be prioritized for sensor deployment. 86 FRAMEWORK TO PRIORITIZE URBAN AREAS FOR SENSOR DEPLOYMENT 87 The proposed framework, outlined in Figure 2, comprises three stages. The first stage involves 88 stakeholder elicitation to explore potential uses of the network and identify suitable metrics for 89 evaluating sensor locations in terms of these uses. The second stage involves a flood risk analysis 90 thatcombinestheidentifiedmetricswithfloodhazardmodels,enablingsensordeploymentlocations 91 to be considered in the context of possible flood impacts. Stage three evaluates trade-offs between 92 the metrics to investigate how the sensors can best be deployed to serve their multiple potential 93 4 Negri, May 15, 2024 Fig.2.Overviewofthethree-stageframeworktoprioritizefloodsensordeploymentareasthrough stakeholder elicitation, risk analysis, and metric combination and tradeoff analysis. uses. 94 Stage 1: Stakeholder Elicitation 95 Stakeholderelicitationisanestablishedmethodtosystematicallygatherinputsandperspectives 96 fromrelevantpartiesaffectedbyaspecificissueorproject(Reed2008). Thisstageoftheframework 97 has three main goals: (1) identify various flood risk management uses for the data generated by 98 the monitoring network, (2) define a set of corresponding metrics that can be used to evaluate 99 sensorlocationsaccordingtotheseuses,and(3)includespecificmetricsthatfocusonparticularly 100 vulnerable communities with lower capacity to cope and recover from flooding disasters, e.g., 101 becauseoflowerincome(DowandCutter2006;Flanaganetal.2011). Thestakeholderelicitation 102 stage occurs in three phases: 103 Stakeholder Identification 104 Wefollowedtheprinciplesofstakeholderanalysistodefineatwo-stepprocessfordetermining 105 relevantstakeholders(GilmourandBeilin2007;PouloudiandWhitley1997). Thefirststepinvolves 106 brainstormingapreliminarylistofusestoidentifyaninitialsetofstakeholders. Thesestakeholders 107 5 Negri, May 15, 2024 are then consulted to determine further potential uses and associated stakeholders. 108 We identified four key stakeholder categories from the literature on disaster risk management 109 and hazard monitoring (Mojtahedi and Oo 2017; Rabinovici et al. 2022; Mojtahedi and Oo 2017; 110 Fontainha et al. 2017; Cremen and Galasso 2021; Cremen et al. 2022; Webster et al. 2022): 111 government agencies, research institutions, the private sector, and resident representatives . The 112 last category refers to individuals who could speak or act on behalf of their community. While 113 non-exhaustive, these stakeholder categories are key in risk management processes for natural 114 hazards (Scolobig et al. 2014). These categories, which can be further refined into sub-categories 115 or specific entities, can be used to determine the preliminary list of stakeholders. 116 Elicitation of Uses and Metrics 117 Next, stakeholder feedback is collected through the following three overarching questions: 118 1. “How and when would you use flood sensor data to help you in your duties before, during, 119 or after floods?” 120 2. “What metrics could help prioritize the location of flood sensors according to your needs?” 121 3. “Whatsocialvulnerabilitymetricsandrelatedfactorsshouldalsobeconsideredwhendecid- 122 ing on flood sensor placements?” 123 The stakeholder elicitation process must follow established guidelines (Chambers 2002; De- 124 partment for International Development 2002; International Association for Public Participation 125 2004; Reed 2008; Knol et al. 2010; Wates 2014; Hemming et al. 2018; Rabinovici et al. 2022). 126 Forexample,theworkshopsshouldbeconductedinpersonwherepossible,topromoteaneffective 127 exchange of ideas and foster discussion among participants (Rabinovici et al. 2022). The number 128 of participants should be limited to 40-50 to allow enough time for everyone to share their views. 129 Participants can further be divided into smaller working groups (e.g., ∼five people (Chambers 130 2002)) to promote richer discussions and interactions (Wates 2014). Workshops must be guided 131 by moderators who clearly explain the workshop’s goals and format and record the outputs of 132 discussions using a visible medium (e.g., a whiteboard). 133 6 Negri, May 15, 2024 Following Rabinovici et al. (2022), stakeholders should spend 5 to 10 minutes independently 134 formulatingtheirresponsestoeachquestion,whicharethensharedwiththewider(working)group 135 to develop a set of collective insights. These insights are used to define interim sets of Use-Case 136 Metrics, (addressing question two) and Social Vulnerability Metrics (addressing question three). 137 AnexampleofaUse-CaseMetriccouldbethevolumeofvehiculartrafficonroadsandhighways, 138 corresponding to the use of sensors in helping a local transportation department monitor traffic 139 during flooding. An example of a Social Vulnerability Metric could be the income to indicate 140 neighborhoods with low resources and likely disproportionately impacted by floods (Lee et al. 141 2022; Sanders et al. 2023). 142 Thestakeholderanalysisliteraturesuggeststhatconvening residentrepresentatives andgovern- 143 mentagencies togetherinasingleworkshopcouldleadtomorereservedandlessopendiscussions, 144 due to competing expectations and demands (Gilmour and Beilin 2007; Yu and Leung 2018; 145 Blázquezetal.2021). Forexample, governmentagencies mightfearupsetting residentrepresenta- 146 tives,potentiallyleadingtopoliticalrepercussions. Itisthereforerecommendedthattheelicitation 147 workshopsareconductedintworounds: (1)aninitialroundconsistingofoneworkshopfor resident 148 representatives and another for the remaining stakeholder categories, and (2) a second combined 149 workshop that is used to finalise the set of metrics, reflecting diverse perspectives. In the second 150 combined workshop, stakeholder categories should be equally represented regarding participants 151 to ensure a balanced outcome (Knol et al. 2010). 152 Selection of the Final Set of Prioritization Metrics 153 TheinterimsetsofUse-CaseMetricsandSocialVulnerabilityMetricsarerefinedbyrequiring 154 each participant to select the three most important metrics from each set. These choices are 155 indicated on visible media, e.g., using voting dots on a whiteboard. The 𝑛metrics chosen the 156 most from each set are used for further analysis. We define the vector 𝑣𝑣𝑣∈R𝑚to contain these 𝑚 157 metrics. Thus, 𝑚=2·𝑛for𝑛Use-Case Metrics and 𝑛Social Vulnerability Metrics. By selecting 158 alarger𝑚,thesensorlocationdecision-makingprocessaccountsforawiderrangeofnetworkuses 159 and a broader representation of social vulnerability. However, very large 𝑚values could cause 160 7 Negri, May 15, 2024 problems in a subsequent post-processing activity, as explained in a following section. Finally, 161 each stakeholder assigns an importance coefficient ( 𝛼𝑗) to each of the 𝑚metrics in both sets, in 162 line with their p