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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Harnessing Real-Time Weather Data for Improved Bridge Inspection after Flood Events</title>
      <link>https://rip.trb.org/View/2590605</link>
      <description><![CDATA[The research aim is to harness near real-time weather data to improve guidance for bridge inspections following rainfall events. Recent rainfall events in Indiana have washed out bridges, resulting in broken transportation networks and loss of life. The National Oceanic and Atmospheric Administration (NOAA) provides near real-time weather information that integrates data from radar, rain gauges, satellites, numerical predictions, and other observations (i.e., lightning, surface, upper air). This research will investigate the use of this publicly available data to trigger bridge inspections, with the aim of improving
public safety.
]]></description>
      <pubDate>Tue, 19 Aug 2025 15:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2590605</guid>
    </item>
    <item>
      <title>RES2020-23: Peak Flow Estimation in Urban Areas - PART 1</title>
      <link>https://rip.trb.org/View/2487329</link>
      <description><![CDATA[16. Abstract

This project addressed the need for updating the existing peak flow equations for urban basins in Tennessee. After reviewing the current state of the art, the work reported herein focuses on unraveling the complex, interacting effects that non-stationary precipitation, evolving urbanization levels, and spatial patterns in land development, rainfall, as well as antecedent conditions, all have on the hydrologic response or urbanizing basins. Potential uncertainties and biases in the estimation of extreme rainfall quantiles (IDF-DDF values), due to the low density of weather stations and the use of totalized rainfall data, and in the frequency analysis of frequent floods, due to using annual maxima instead of partial duration (peaks over threshold) series, are also investigated.

All urban basins in Tennessee have experienced growth in the amounts of developed areas in the past 20 years, and there is a significant increase in the frequency of extreme rainfall events in the region. Using rainfall data with the 15-minute resolution typically available in the U.S. introduces a negative bias that is highly variable across stations, while the low density of rain gauges increases the uncertainty in IDF-DDF values.

We derive a novel urbanization index based on hydrologic connectivity that, in contrast with the traditional approach of using percentage of impervious area (IA), is able to reflect the hydrologic effects of different spatial distributions of urbanized patches within a watershed. This index outperforms IA when used as an explanatory variable in regression equations for predicting urban peak flows.

A methodology to perform continuous hydrologic simulation with artificial neural networks is also proposed to investigate the effects of changing land cover, excluding concurrent effects of trends in precipitation.

]]></description>
      <pubDate>Tue, 07 Jan 2025 10:19:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487329</guid>
    </item>
    <item>
      <title>Evaluation of Moisture Susceptibility of Full-Depth Reclamation (FDR) Mixes</title>
      <link>https://rip.trb.org/View/2414048</link>
      <description><![CDATA[The Virginia Department of Transportation (VDOT) is committed to enhancing the resilience of Virginia’s transportation network in the face of changing climatic conditions and extreme weather events.  The VDOT Resilience Plan outlines a comprehensive strategy to incorporate resilience measures into transportation planning, project development, delivery, operations, maintenance, and asset management.  As part of this plan, VDOT is exploring a wide range of innovative solutions, including the use of advanced materials, improved construction methods, adaptive design criteria, and nature-based solutions, to align with its resilience goals.  Full-Depth Reclamation (FDR) is one such technique that can offer numerous benefits and has the potential to significantly contribute to VDOT’s resilience objectives. Even though FDR, as a stabilized material, possesses higher strength and stiffness compared to unbound base/subbase layers, it is still unknown whether it is durable under fully or partially saturated conditions and retains high strength at increased moisture content during water-driven climatic events.  Thus, there is a need to thoroughly evaluate the susceptibility of FDR mixes to moisture-induced damage to ensure their long-term performance and resilience under water-driven climatic events. ]]></description>
      <pubDate>Thu, 08 Aug 2024 09:03:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2414048</guid>
    </item>
    <item>
      <title>Investigating Real Storms and the Impact of Potential Climate Change Adaptations</title>
      <link>https://rip.trb.org/View/2387094</link>
      <description><![CDATA[Previous work on extreme storms has focused on changes in the 24-hour precipitation depth. The storm intensity, the distribution of rainfall, and antecedent conditions are also important for urban stormwater management. Changes in the precipitation distribution upon which infrastructure was designed determines the risk communities encumber with respect to flooding, property damage, and human safety. The research team propose to quantify trends across Minnesota in storm intensity, storm duration, and distribution of rainfall over a range of time periods to re-evaluate the assumptions for design storms. The proposed research will also investigate the change in infiltration through pervious surfaces and other green infrastructure in response to re-evaluated extreme storm intensity and distribution of rainfall. In addition, different watershed adaptation strategies will be evaluated for relative performance and cost, with a focus on strategies sensitive to precipitation intensity, such as infiltration in pervious areas. The objectives of the proposed research are to (1) quantify stormwater system vulnerability to flooding for a range of re-evaluated precipitation maximum storm intensity, storm duration, and distribution of rainfall and (2) quantify the relative efficacy and costs of green infrastructure and conventional engineering adaptation approaches to mitigate flooding as contrasted in three communities exhibiting different growth patterns, forms of stormwater networks, and in differing climate regions of Minnesota. The proposed project will use modeling and data to improve forecasting of the magnitude of impacts from extreme weather events, understand impacts to transportation infrastructure holistically when facing climate change, and assess flood management strategies on a systemic level to clarify best practices for roadway and road-adjacent infrastructure resilience.]]></description>
      <pubDate>Mon, 03 Jun 2024 12:05:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2387094</guid>
    </item>
    <item>
      <title>Distributed Rainfall-Runoff Models to Support the Planning and Design of Resilient Transportation Systems



</title>
      <link>https://rip.trb.org/View/2381699</link>
      <description><![CDATA[Distributed rainfall-runoff models (DRRMs) are an important tool for assessing future precipitation impacts to the transportation system. With climate change raising the risk of more intense and frequent storms, water-related stressors, such as flooding, in-stream structure scour, and aggradation, could likely worsen for transportation corridors. DRRMs provide the capabilities needed to assess the impacts of future rainfall patterns and amounts on the transportation system. As detailed in NCHRP Synthesis 602: Resilient Design with Distributed Rainfall-Runoff Modeling, such modeling tools are cited in 54% of the hydrological design guidelines from all state transportation agencies, including state departments of transportation (DOTs) and pertinent state agencies, yet only 33% of the responding DOTs (16 out of 48) reported applying DRRMs. Without DRRM guidelines, transportation planning and modeling professionals will be limited in effectively leveraging innovative data sources, including the ATLAS 15 future precipitation dataset under development by the National Oceanic and Atmospheric Administration (NOAA).

Accordingly, research is needed to begin transforming the state of practice in transportation hydraulic and hydrologic modeling and provide essential guidelines needed to fully leverage the ATLAS 15 future precipitation data and other future data sources. 

The objective of this project is to develop resources that will facilitate state transportation agency adoption of DRRMs for planning and design.]]></description>
      <pubDate>Mon, 20 May 2024 21:14:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381699</guid>
    </item>
    <item>
      <title>Increasing Understanding of Weather Extremes and Enhancing Safety of Rural and Tribal Areas using Wireless Smart Sensors and Human-Environment-Data Interfaces using Augmented Reality (AR)</title>
      <link>https://rip.trb.org/View/2291296</link>
      <description><![CDATA[This project aims to develop an interface between users and data in the context of low-cost deployment of sensors that can be tested to collect both rain and flooding during significant post-wildfire flooding events. The system will be wireless and validated with a local company in New Mexico, High Water Mark (HWM) LLC, with expertise in flooding. The support of Prospect Solutions, another participating company, will enable the research team to develop a transportation-directed tool that can be used for other aspects critical to durability. This project proposes using Low-cost Efficient Wireless Intelligent Sensors (LEWIS) that can be moved and installed at very low cost, measuring both rain and flooding levels (elevation). Real-time data from such sensors can inform the population about the flooding with 10-20 minutes notice. The LEWIS are connected to the internet with hotspots and their design and installation are incremental so they can be changed by the owners. The first step will be to design and demonstrate a rain/flooding data interface system using simulated rainfall and flooding using indoor facilities at UNM. The second step will be located outdoors near a creek in the mountains to validate the power independence of such a system and to obtain field data from a rain and/or flooding event. The third step is to create a simulation of the sensor-AR interface to collect/identify thresholds of emergencies from the experts (HWM LLC and Prospect Solutions) and subsequently from the community in a workshop.]]></description>
      <pubDate>Thu, 16 Nov 2023 17:45:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2291296</guid>
    </item>
    <item>
      <title>Michigan Hydrologic Calculation Procedures</title>
      <link>https://rip.trb.org/View/1993820</link>
      <description><![CDATA[The Federal Highway Administration (FHWA), the Michigan Department of Environment, Great Lakes, and Energy (EGLE) and the Michigan Department of Transportation (MDOT) reviewed the approved procedures for calculating discharges from simulated
Michigan rainfall events. The current hydrologic methods rely on older data sets and new data is available for consideration. While EGLE and MDOT understand how these procedures need to be updated, limited staff resources are focused on critical assessment tasks and cannot be diverted to quickly update these methods. The updating of these methods is critical because discharges are currently used to assess flood conditions for mapping, conveyance design, and flood mitigation. EGLE and MDOT have a desire to incorporate modern data sets into existing methodologies to improve calculated discharge results.]]></description>
      <pubDate>Thu, 14 Jul 2022 13:00:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1993820</guid>
    </item>
    <item>
      <title>Investigation of the Impact of Rainfall Patterns on Highway Slope Instability</title>
      <link>https://rip.trb.org/View/1904910</link>
      <description><![CDATA[Highway embankments are one of the most crucial elements of the transportation
infrastructure system in the United States. Therefore, keeping the integrity of the highway
slopes is of utmost importance for economic sustainability of the country. However,
because of the variation of the environmental condition and the presence of unsuitable
soil that constitutes many of the highway embankments often pose serious threat to these infrastructures' stability and cause property damages and casualties. Highway
embankments constructed with unsuitable soil, especially expansive clayey soil are
susceptible to reduction in the shear strength properties, and eventually cause the failure.
The southern Texas (greater Houston region) has been experiencing multiple natural
disasters and subsequent deterioration of the transportation infrastructures (e.g.,
pavement distress, slope failure etc.). Among these, the variable rainfall pattern is one of
the major causes of slope instability. Additionally, substantial heterogeneities of
geological composition have been observed in this region. In the proposed study, the research team aims to study the rainfall characteristics of the southern part of Texas and develop the rainfall
pattern for this region. The typical measurement of rainfall is executed by rain gauge
network, which is straightforward to estimate the surface precipitation and has significant
spatial variation. To achieve greater resolution in rainfall measurement, this proposed
research will incorporate the weather radar networks. Furthermore, the team aims to develop a
geotechnical database containing information such as sub-soil characteristics (e.g.,
physical, hydraulic, strength), slope geometrical configuration, and other pertinent
information of highway slopes for this region to synthesize the hydro-geotechnical
analysis.]]></description>
      <pubDate>Thu, 20 Jan 2022 16:31:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1904910</guid>
    </item>
    <item>
      <title>Artificial Intelligence (AI) for Building a Landslide Inventory &amp; Advanced Landslide Warning System in Pennsylvania</title>
      <link>https://rip.trb.org/View/1902393</link>
      <description><![CDATA[The purpose of this project is to develop artificial intelligence (AI) models for advance warning of rainfall-induced landslides for unstable slopes above or below state maintained roadways in Pennsylvania. Landslides are a significant geologic hazard throughout most of southwestern Pennsylvania and in certain other parts of the state (Delano and Wilshusen 2001). The average annual direct and indirect cost of landslides is in the tens of millions of dollars in the state. Landslides cause damage to utilities, buildings, and transportation routes, which, in turn, creates travel delays and other side effects. For example, during the rainy season of 2019, PennDOT’s Pittsburgh district concurrently dealt with 95 landslides, among which the Reis Run slide resulted in the
closure of Reis Run Road on May 31, 2019, causing significant safety risk and inconvenience to the traveling public and local residents. As more land is being developed, with more frequent extreme rainfalls associated with the climate change, an increased frequency of rainfall-induced landslides is likely in the coming decades. Roadway reconstruction costs, travel delays, and other side effects could be significantly reduced if an advanced warning system of rainfall-induced landslides could be provided to, and implemented by, transportation officials to address rainfall-induced landslides before they affect the safety, inconvenience and cost to the public.
]]></description>
      <pubDate>Mon, 10 Jan 2022 14:54:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1902393</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 53-11. Resilient Design with Distributed Rainfall-Runoff Modeling</title>
      <link>https://rip.trb.org/View/1853018</link>
      <description><![CDATA[According to the National Oceanic and Atmospheric Administration, the number and cost of weather and climate-related disasters are increasing in the United States due to a combination of increased exposure, vulnerability, and the fact that climate change is increasing the frequency of extreme events. The increased frequency of hurricanes and severe storm events are requiring state departments of transportation (DOTs) to consider how to anticipate, plan for, and adapt to these changing conditions. In addition, DOTs are considering how to withstand, respond to, and recover more rapidly when disruptions occur. For these reasons, more complex hydrologic modeling that allows for scenario testing and impact assessment is needed.

Distributed rainfall-runoff methods are models that use physical equations to describe rainfall patterns and water movement to create flow rates. Distributed rainfall-runoff models are hydrologic models that simulate runoff production, transport, and accumulation in waterways. These models split the watershed into small elements that are used to calculate and track infiltration and movement of runoff on the ground by using processed based equations.

Increased computing power and modeling efficiency have made distributed rainfall-runoff models more cost effective for engineering projects. Because distributed models are process based, they also have more flexibility than statistical methods because they are not restricted to historical data. As a result, there has been increased interest among engineering practitioners in using distributed rainfall-runoff models to create more resilient designs. However, there has been little documented guidance on applying modern distributed rainfall-runoff models to help engineers use them in the highway design process.

The objective of this synthesis is to document state DOT use of distributed rainfall-runoff models. The synthesis will focus on the use of distributed rainfall-runoff methods for hydrologic analyses for the planning, design, and operation of bridges and roadway projects.]]></description>
      <pubDate>Tue, 18 May 2021 12:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1853018</guid>
    </item>
    <item>
      <title>Incorporation of Climatic and Hydrologic Nonstationarity into FDOT Planning and Design Guidelines &amp; Processes
</title>
      <link>https://rip.trb.org/View/1844151</link>
      <description><![CDATA[The research is focused on potential modifications to current manuals of practice being used by Florida Department of Transportation (FDOT) for transportation project design. When possible, guidance will also be provided on relevant planning practices that may be in use by FDOT. With respect to transportation infrastructure (TI) designs, the research will identify which standards may warrant modifications to account for implications of future climate change, especially sea level rise and changes to extreme rainfall. The final report of this research project will recommend appropriate nonstationarity methods and data sets for future use by FDOT engineers and consultants for future planning and design of TI.]]></description>
      <pubDate>Tue, 11 May 2021 15:23:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844151</guid>
    </item>
    <item>
      <title>Predicting Roadway Washout Locations During Extreme Rainfall Events</title>
      <link>https://rip.trb.org/View/1765389</link>
      <description><![CDATA[Recent extreme rainfall events have revealed the transportation network’s vulnerabilities to road washouts. Currently, North Carolina Department of Transportation (NCDOT) reacts to these problems as are reported from the field. This inability to predict where washouts are likely to occur leads to long response times and inefficient positioning of resources. The availability of high quality statewide elevation data, historical rainfall records and advances in computer processing presents the opportunity to modify and develop programs to predict where washouts are likely to occur during extreme rainfall events. The purpose of this project is to develop models and test several approaches for predicting crossing washouts based on forecasted rainfall. A team of North Carolina State University Department of Biological and Agricultural Engineering (NCSU BAE) engineers will first characterize and analyze historical washouts during extreme events. Then, detailed Hydrologic Engineering Center-hydrologic modeling system (HEC-HMS) models will be developed and calibrated and validated for one watershed in each physiographic region. A user interface will be created to run the models using forecasted rainfall, relate the predicted discharge to potential washouts using water surface elevation-discharge relationships, and then output the results for display in a geographic information systems (GIS) map. The model output for a large number of historical events will then be used to test different machine learning algorithms for their ability to predict discharge and potential washout locations. The information on historical washouts and the model predictions will be used to develop a network of “safe” routes for each watershed. The results will help determine if existing hydrologic models can be leveraged to accurately predict potential washout locations and to evaluate if machine learning technology can be employed for accurate flood prediction. This project has the potential to substantially enhance NCDOT’s ability to respond to storm events and position resources appropriately. Results will be disseminated in NCDOT meetings, a training workshop for NCDOT personnel, and through extension factsheets and academic publications.]]></description>
      <pubDate>Tue, 26 Jan 2021 08:03:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1765389</guid>
    </item>
    <item>
      <title>Predictive Deep Learning for Flash Flood Management</title>
      <link>https://rip.trb.org/View/1727643</link>
      <description><![CDATA[This research uses deep learning methods, along with geospatial data from the USGS National Map and other public geospatial data sources, to develop forecasting tools capable of assessing water level rate of change in high risk flood areas. These tools build on existing models developed by the USGS, FEMA, and others, and are used to determine evacuation routing and detours to mitigate the potential for loss of life during flash floods. The project scope includes analysis of publically available flood data along a river basin as part of a pilot project in Missouri. These data are then used to determine the rate of rise based on projected rainfall totals. This rate of rise is used to model evacuation or detour planning modules that can be implemented to assure the safety of the community and highway personnel, as well as the safe and secure transport of goods along public roadways. These modules can be linked to existing real-time rainfall gauges and weather forecasts for improved accuracy and usability. The transportation safety or disaster planner can use these results to produce planning documents based on geospatial data and information to develop region-specific tools and methods.]]></description>
      <pubDate>Fri, 07 Aug 2020 17:44:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1727643</guid>
    </item>
    <item>
      <title>RES2020-23: Updating equations for peak flow estimation in urban creeks and streams of Tennessee</title>
      <link>https://rip.trb.org/View/1717008</link>
      <description><![CDATA[Engineers need to predict flood magnitude for different frequencies (return periods) in order to design infrastructure, for management and zoning, for emergency response, and to understand channel instability and other environmental impacts. In cities, where the risk to lives and property is higher, the hydrological response can be non-stationary because of urbanizing trends, changes in extreme precipitation, and development of storm water control measures.
As a result, Tennessee Department of Transportation (TDOT) proposed Research Needs Statement Number 25, which states that: “TDOT uses equations developed by the United States Geological Survey (USGS) to estimate peak flows through all structures with a drainage area greater than approximately 500 acres. Equations for urban areas have not been updated since 1984 and do not take advantage of updates in estimation techniques and an additional 30 years of observed flow data to increase accuracy. Tennessee is currently experiencing exponential urban growth and updating the equations will provide more accurate peak flows resulting in more accurate drainage structure design.”
The overall problem in the case of Tennessee involves multiple aspects: Previous work on the topic is aged and existing equations do not consider non-stationarity in either land use or rainfall extremes; few cities have gaged urban watersheds, and all of these are located within only three of the five main physiographic zones within TN; number of stations, record lengths and the range of variability in watershed characteristics needed for traditional regression analyses are very limited; there is a need to disentangle the effects of urbanization trends from any potential changes in extreme precipitation, while predictive equations should explicitly incorporate both these drivers, so as to be useful in the future, and; TDOT requires an automated decision support tool for calculation of estimates of flood-flow frequency and magnitude in urban areas of TN.
This proposal offers innovative approaches to better understand the hydrologic response of urbanizing watersheds in Tennessee, in order to obtain the best possible predictive equations for urban peak flows, in a context of limited streamflow information, both in time (short records) and space (few stations, that happen to be spatially clustered). 
]]></description>
      <pubDate>Mon, 29 Jun 2020 17:50:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/1717008</guid>
    </item>
    <item>
      <title>Identifying High-Risk Areas During Precipitation Events Support of NCDOT Stormwater Quality Monitoring</title>
      <link>https://rip.trb.org/View/1480740</link>
      <description><![CDATA[The State Climate Office of North Carolina (SCO) and North Carolina Department of Transportation (NCDOT) have previously partnered on the development of a comprehensive precipitation alert system, which includes a detailed mapping system and rainfall monitoring alert services. This collaborative project has been estimated to save over 110,000 work hours per year, and has won several state and national awards.  This proposal will enhance and leverage that partnership by identifying high-risk areas during or shortly after the occurrence of heavy precipitation events as specified by NCDOT engineers. These high-risk zones will be highlighted on a map interface and/or via an alert, and will be defined by the historical likelihood of obtaining that same precipitation amount within a specified time period at a particular location. For example, one scenario could be: If the precipitation total for the previous 24 hours in Raleigh, NC has historically only occurred once every 25-years, that particular area could be designated as a high-risk region. In addition, a time series plot will display the year-to-date accumulation of rainfall for the current year as compared to the normal (30-year average) year-to-date rainfall accumulation. These additional features will help NCDOT better prioritize and deploy resources for flood and runoff mitigation
]]></description>
      <pubDate>Tue, 22 Aug 2017 14:22:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1480740</guid>
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