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Floods and droughts significantly affect agricultural activities and pose a threat to food security by subsequently reducing agricultural production. The impact of flood events is distributed disproportionately among agricultural communities based on their socio-economic fabric. Understanding climate-related hazards is critical for planning mitigation measures to secure vulnerable communities. This study introduces a holistic approach for evaluating the combined risks associated with drought and flood hazards for agricultural communities in the United States. It accomplishes this by merging social vulnerability indicators with data on drought and flood exposure, enabling the identification of the most susceptible agricultural communities. The research seeks to offer valuable insights into the vulnerability of agricultural communities across the United States. It fills a vital research gap by conducting a comprehensive nationwide assessment of social vulnerability, considering expected annual losses related to both flood and drought hazards, and amalgamating social vulnerability with these expected annual losses. The analyses were conducted by adapting datasets and methodologies that are developed by federal institutions such as FEMA, USACE, and USDA. The study identified the 30 most socially vulnerable counties and assessed their exposure to drought and flooding, finding that Mendocino, Sonoma, Humboldt, El Dorado, Fresno, and Kern counties in California had the highest drought exposure and expected annual losses, with Humboldt (CA) and Montgomery (TX) having the highest combined risk. The study estimated over $1 billion in crop damage, with California experiencing the greatest losses, primarily affecting a diverse range of crops, while the Midwest was primarily impacted in terms of major crop types. The findings of this study can serve as supportive information for policymakers to better understand climate risks in agricultural communities and identify where risk mitigation activities should be allocated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Data-driven models for water body extraction have experienced accelerated growth in recent years, thanks to advances in processing techniques and computational resources, as well as improved data availability. In this study, we modified the standard U-Net, a convolutional neural network (CNN) method, to extract water bodies from scenes captured from Sentinel-1 satellites of selected areas during the 2019 Central US flooding. We compared the results to several benchmark models, including the standard U-Net and ResNet50, an advanced thresholding method, Bmax Otsu, and a recently introduced flood inundation map archive. Then, we looked at how data input types, input resolution, and using pre-trained weights affect the model performance. We adopted a three-category classification frame to test whether and how permanent water and flood pixels behave differently. Most of the data in this study were gathered and pre-processed utilizing the open access Google Earth Engine (GEE) cloud platform. According to the results, the adjusted U-Net outperformed all other benchmark models and datasets. Adding a slope layer enhances model performance with the 30 m input data compared to training the model on only VV and VH bands of SAR images. Adding DEM and Height Above Nearest Drainage (HAND) model data layer improved performance for models trained on 10 m datasets. The results also suggested that CNN-based semantic segmentation may fail to correctly classify pixels around narrow river channels. Furthermore, our findings revealed that it is necessary to differentiate permanent water and flood pixels because they behave differently. Finally, the results indicated that using pre-trained weights from a coarse dataset can significantly minimize initial training loss on finer datasets and speed up convergence., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

It is critical to obtain accurate flood extent predictions in a timely manner in order to reduce flood-related casualties and economic losses from floods with greater magnitudes and fall outside the handling capacity of our existing mitigation systems. Running a real-time flood inundation mapping model is helpful in supporting quick response decisions for unplanned floods, such as how to distribute limited resources and labor so that the most flood-prone areas receive adequate mitigation efforts and how to execute evacuations that keep people safe while causing the least amount of unneeded disruption. Most inundation systems, on the other hand, are either overly demanding in terms of data and computing power or have limited interaction and customization with various input and model configurations. This paper describes a client-side web-based real-time inundation mapping system based on the Height Above the Nearest Drainage (HAND) model. The system includes tools for hydro-conditioning terrain data, modifying terrain data, custom inundation mapping, online model performance evaluation, and hydro-spatial analyses. Instead of only being able to work on a few preprocessed datasets, the system is ready to run in any region of the world with limited data needs (i.e., elevation). With the system's multi-depth inundation mapping approach, we can use water depth measurements (sensor-based or crowdsourced) or model predictions to generate more accurate flood inundation maps based on current or future conditions. All of the system's functions can be performed entirely through a client-side web browser, without the need for GIS software or server-side computing. For decision-makers and the general public with limited technical backgrounds, the system provides a one-stop, easy-to-use flood inundation modeling and analysis tool., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Agricultural lands are often impacted by flooding, which results in economic losses and causes food insecurity across the world. Due to the world's growing population, land-use alteration is frequently practiced meeting global demand. However, land-use changes combined with climate change have resulted in extreme hydrological changes (i.e., flooding and drought) in many areas. The state of Iowa has experienced several flooding events over the last couple of decades (e.g., 1993, 2008, 2014, 2016, 2019). Also, agribusiness is conducted across 85% of the state. In this research, we present a comprehensive assessment for agricultural flood risk in the state of Iowa utilizing most up-to-date flood inundation maps and crop layer raster datasets. The study analyzes the seasonal variation of the statewide agricultural flood risk by focusing on corn, soybean, and alfalfa crops. The results show that over $230 million average annualized losses estimated at statewide considering studied crop types. The crop frequency layers and corn suitability rating datasets are investigated to reveal regions with lower or higher productivity ratings. The study founds nearly half a million acres of cropland is under 2-year return period flood zone. Additionally, a data-driven flood model, Height Above the Nearest Drainage (HAND), is used to analyze performance against the FEMA maps. We found that the HAND flood maps performed with the correlation of 0.93 and 0.94 for 100-year and 500-year flood events regarding to the FEMA maps., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Flooding is one of the most frequent natural disasters, causing billions of dollars in damage and threatening vulnerable communities worldwide. Although the impact of flooding can never be diminished, minimizing future losses is possible by taking structural or non-structural mitigation actions. Mitigation applications are often costly practices. However, they can be more feasible for long-term planning and protection. On the other hand, selecting a feasible option requires a comprehensive analysis of potential risk and damages and comparing the costs and benefits of different mitigation types. This paper presents a web-based decision support framework called Mitigation and Damage Assessment System (MiDAS) that analyzes flood risk impacts and mitigation strategies at the community and property-level with the goal of informing communities on the consequences of flooding and mitigation alternatives and encouraging them to participate in the community rating system. The framework utilizes regulatory flood inundation maps, damage functions, property information, scenario-based climate projections, and mitigation inputs and guidelines from the Federal Emergency Management Agency (FEMA) and the United States Army Corps of Engineers (USACE). It will help users select the appropriate flood mitigation measures based on various characteristics (e.g., foundation type, occupancy, square footage) and provide cost estimates for implementing measures. The system also provides a decision tree algorithm for analyzing and representing the mitigation decision by reviewing existing guidelines (e.g., FEMA, USACE). We analyzed the community-level mitigation for three major cities in Eastern Iowa (Cedar Falls, Cedar Rapids, and Waterloo) and found certain measures (e.g., wet/dry floodproofing) are cost-effective for community-level mitigation. Implementation of mitigation measures can reduce the property's vulnerability and improve the response to a flooding event., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Natural disasters, such as flooding, can cause severe social, environmental, and economic damage to a community. Transportation infrastructure plays an essential role in flood response and recovery efforts. However, flooding may disturb road functionality and generate direct and indirect adverse impacts, including the loss of access to essential services. This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses. We assessed the disruption of transportation networks on the accessibility to critical amenities (e.g., hospitals) under 100 and 500-year flood scenarios. Our analysis methodology leads toward the development of an integrated real-time decision support system that will allow decision-makers to explore "what if" flood scenarios to identify vulnerable areas and population in their authority. These analyses could promote possible improvements (e.g., temporary relocation of critical services) to mitigate the consequences of road system failure during flooding. Due to varying environmental conditions at specific locations and effects on road topology under flood events, the results show differential impacts in edge and node losses as well as access to critical services. Results indicate that floods can lead to edge losses of up to 18%, and not only large cities but also some small cities can experience significant vulnerability to flooding. Some new or reconstructed bridges have failed to operate during analyzed flood events. During the 100 and 500-year flood return periods, the total number of inaccessible bridges within the selected cities is 184 and 294, respectively. Our work found that the shortest path length to the closest critical amenity under baseline condition can flip to the second or higher-orders during flooding. Many critical amenities have been found at risk of flooding in the studied cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Groundwater supplies drinking water for over one-third of all Americans. However, with aquifers stressed by overdraft, contamination from land use, and the hydrologic impacts of climate change, identifying reliable sources for new wells is increasingly challenging. Well forecasting is a process in which potential groundwater resources are evaluated for a location of interest. While this process forecasts the depth of each aquifer for a given location, it takes historical groundwater well data from nearby locations into account. Conventionally, well forecasting is done by geological survey professionals by manually analyzing the well data and, that makes the process both time and resource-intensive. This study presents a novel web application that performs well forecasting for any location within the state of Iowa in a matter of seconds utilizing client-side computing instead of expensive professional labor. The web application generates well forecasts by triangulating millions of combinations of historical aquifer depth data of nearby wells stored in a state-level database. The proposed web system also provides water quality information for arsenic, nitrate, and bacteria (total c and fecal coliform) on the same interface with forecasts. The system is open to the public and is aimed to provide a go-to tool for homeowners, well drillers and, authorities to help inform decision-making regarding groundwater well development and water quality monitoring efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

With increasing population and human intervention on the natural environment, hazards are a growing threat, coming in many forms, including floods, droughts, soil erosion, and water pollution. A key approach to mitigate hydrological disaster risk at the community level is informed planning with decision support systems. The literature shows emerging efforts on multi-hazard decision support systems for hydrological disasters and demonstrates the need for an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities. In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. Major contributions of the presented DST include involving the community in environmental decision making by reducing the technical complexity required for analysis, increasing community awareness for the environmental and socio-economic consequences of hydrological hazards, and allowing stakeholders to discover and discuss potential trade-offs to hazardous scenarios considering the limitations in budget, regulations, and technicality. The paper describes the software design approaches and system architecture applied for a modular, secure, and scalable software as well as the framework's intuitive web-based user interfaces for real-time and collaborative data analysis and damage assessment. Finally, a case study was conducted to demonstrate the usability of DST in a formal setting and to measure user satisfaction with surveys., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Sediment accumulation at culverts involves large-scale and interlinked environmental processes that are difficult to address with experimental or physical modeling methods. This article presents an alternative data-driven investigation for shedding insights into these processes. Accordingly, a web-based geovisual analytics application, the IowaDOT platform, was developed, which allows users to explore the complex processes associated with the sediment deposition at culverts. The platform provides systematic procedures for (1) collecting and integrating analytical variables into a single dataset, (2) quantifying the degree of culvert sedimentation using time series of aerial images, (3) identifying drivers that contribute to culvert sedimentation processes from a variety of culvert structural and upstream landscape characteristics using a tree-based feature selection algorithm, and (4) facilitating the understanding of complex spatial and relational patterns of culvert sedimentation processes using multivariate geovisualizations supported by a self-organizing map (SOM). As the outcomes of this study, these patterns identify culvert sedimentation-prone regions in Iowa and quantify empirical relationships between the drivers and culvert sedimentation degrees. A simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedbacks are received., (Copyright © 2019 Elsevier B.V. All rights reserved.)