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The lack of community-relevant flood informational resources and tools often results in inadequate and divergent understandings of flood risk and can impede communities' ability to function cohesively in the face of increasing flood threats. The current study reports on a set of workshops that the authors conducted with various groups (citizens, city engineers and planners, realtors and builders, and media representatives) within a flood prone community to evaluate how novel hydroinformatic tools that include hydrodynamic modeling, geospatial visualization, and socioeconomic analysis can enhance understanding of flood risk and engagement in flood risk mitigation among diverse community members. The workshops were designed to help identify stakeholder preferences regarding key functionality needed for integrated hydroinformatic technologies and socioeconomic analyses for flood risk reduction. Workshop participants were asked to use and comment on examples of prototype flood risk informational tools, such as: (1) flood damage estimation tool, (2) drivability and emergency accessibility tool, and (3) community-scale social and economic metrics tool. Data gathered from workshops were analyzed using qualitative analysis based on a grounded-theory approach. Data were coded by hand based on themes identified by the research team and incorporated deviant case analysis to ensure minority opinions was represented. The study results are focused on the following main themes and how flood tools can address them: (1) improving the understanding of flood risk and engagement in flood risk mitigation, (2) reducing the gap between individual and community risk, (3) challenges in communicating flood risk information, (4) enhancing relevance to and engagement of the community, and (5) enabling actionable information. Our research demonstrates the need for community-anchored tools and technologies that can illustrate local context, include local historical and simulated events at multiple levels of community impact, enable analyses by flood professionals while also providing simplified tools of use by citizens, and allow individuals to expand their knowledge beyond their homes, businesses, and places of work. 

Many communities across the USA and globally lack full understanding of the flood risk that may adversely impact them. This information deficit can lead to increased risk of flooding and a lack of engagement in mitigation efforts. Climatic changes, development, and other factors have expedited changes to flood risk. Due to these changes, communities will have an increased need to communicate with a variety of stakeholders about flood risk and mitigation. Lafayette Parish, Louisiana, USA, having recently experienced a major flood event (the 2016 Louisiana Floods), is representative of other communities experiencing changes to flood impacts. Using focus groups, this study delves into better understanding the disconnect between individual and community perceptions of flood risks, and how emerging hydroinformatics tools can bridge these gaps. Using qualitative analysis, this study evaluated the resources individuals use to learn about flooding, how definitions of community impact flood mitigation efforts, how individuals define flooding and its causes, and where gaps in knowledge exist about flood mitigation efforts. This research demonstrates that individuals conceive of flooding in relationship to themselves and their immediate circle first. The study revealed division within the community in how individuals think about the causes of flooding and the potential solutions for reducing flood risk. Based on these results, we argue that helping individuals reconceive how they think about flooding may help them better appreciate the flood mitigation efforts needed at individual, community, and regional levels. Additionally, we suggest that reducing gaps in knowledge about mitigation strategies and broadening how individuals conceive of their community may deepen their understanding of flood impacts and what their community can do to address potential challenges. 

Soil moisture (SM) plays a significant role in determining the probability of flooding in a given area. Currently, SM is most commonly modeled using physically-based numerical hydrologic models. Modeling the natural processes that take place in the soil is difficult and requires assumptions. Besides, hydrologic model runtime is highly impacted by the extent and resolution of the study domain. In this study, we propose a data-driven modeling approach using Deep Learning (DL) models. There are different types of DL algorithms that serve different purposes. For example, the Convolutional Neural Network (CNN) algorithm is well suited for capturing and learning spatial patterns, while the Long Short-Term Memory (LSTM) algorithm is designed to utilize time-series information and to learn from past observations. A DL algorithm that combines the capabilities of CNN and LSTM called ConvLSTM was recently developed. In this study, we investigate the applicability of the ConvLSTM algorithm in predicting SM in a study area located in south Louisiana in the United States. This study reveals that ConvLSTM significantly outperformed CNN in predicting SM. We tested the performance of ConvLSTM based models by using a combination of different sets of predictors and different LSTM sequence lengths. The study results show that ConvLSTM models can predict SM with a mean areal Root Mean Squared Error (RMSE) of 2.5% and mean areal correlation coefficients of 0.9 for our study area. ConvLSTM models can also provide predictions between discrete SM observations, making them potentially useful for applications such as filling observational gaps between satellite overpasses.