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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Peak Flow Regression Equations for Small Drainage Basins in Central and Eastern Montana</title>
      <link>https://rip.trb.org/View/2617192</link>
      <description><![CDATA[Accurate peak-flow rates are needed by the Montana Department of Transportation (MDT) to properly size culverts and bridges on highway stream crossings. For stream crossings with drainage areas less than one square mile, one of the current methods available for estimating peak-flow rates is the set of Nallick peak-flow regression equations in the MDT hydraulics manual (MDT, 2022).

The Nallick regression equations as presented in MDT (2022) use drainage area, average annual precipitation, and 25-year 1-hour rainfall intensity to estimate peak streamflow for 2, 5, 10, 25, 50, and 100-year return intervals and are applicable to small drainage basins (less than 1 square mile) in the plains east of the Continental Divide. The equations were developed using data collected through 1988. Since 1988, more advanced methodologies have emerged for determining the three explanatory variables (drainage area, average annual precipitation, and 25-year 1-hour rainfall intensity) used in the equations. Furthermore, additional peak-flow data collected on small drainage basins and improved methods of peak-flow frequency analysis offer opportunities to improve the streamgage peak-flow frequency estimates used to develop the regression equations. Generalized least-squares (GLS) analysis and machine learning methods offer further potential to improve the mathematical development of the equations.

Records in the Transportation Research Board (TRB) database have emphasized the need for accurate peak-flow information to use in hydrologic modeling and infrastructure design. MDT and the U.S. Geological Survey (USGS) have been addressing this deficiency in peak-flow information by maintaining a crest-stage gage (CSG) network in Montana that has been collecting peak-flow data since 1955 (Sando, 2021). CSGs are simple streamgages that only record the peak stage between visits to the gage. This CSG system is especially important for collecting data in small drainage basins that are often overlooked by continuous streamflow gages. Part of the goal of the CSG network is to collect data for developing peak-flow regression equations at ungaged sites. Peak-flow variability in Montana generally increases as drainage area decreases (Sando, 2021). This variability emphasizes the need for updated regression equations that can effectively predict peak-flow rates in smaller drainage basins.

The work proposed in this project will produce updated regression equations to replace the Nallick peak-flow equations. Project tasks include camera monitoring on small streamgages, Lidar-derived basin delineations, calculation of basin characteristics, and peak-flow frequency analysis to supply the inputs needed for updated regression equations. The updated equations will be derived using generalized or weighted least squares methods and informed by exploratory machine learning analysis. By integrating advanced methods for estimating the explanatory variables and utilizing the extensive peak-flow data collected through the CSG network, this project has the potential to enhance the accuracy and reliability of peak-flow predictions for small drainage basins. Ultimately, this project aims to provide MDT with improved tools for infrastructure design, ensuring that stream crossings are appropriately sized to accommodate the peak-flows occurring in small drainage basins.]]></description>
      <pubDate>Mon, 03 Nov 2025 11:55:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2617192</guid>
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    <item>
      <title>RES2020-23: Peak Flow Estimation in Urban Drainage Areas - PART 2</title>
      <link>https://rip.trb.org/View/2487330</link>
      <description><![CDATA[In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. The study area included streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina. Regression equations were developed to predict streamflows corresponding to the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual excedance probabilities (AEPs) and were incorporated into the StreamStats application.  In generalized least-squares (GLS) regression, the base-10 logarithm of drainage area, the percentages of the streamgage basins in developed land use and the percentages of the streamgage basins in the Piedmont and Ridge and Valley level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area.  Pseudo R-squared of the regression equations ranged from 0.86, or 86 percent, for the 0.5 and 0.2 AEPs (the 2- and 5-year floods) to 0.71, or 71 percent, for the 0.002 AEP (the 500-year flood).  The average variance of prediction (in log base 10 units) ranged from 0.023 for the 0.2 and 0.1 AEPs to 0.05 for the 0.002 AEP.  The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 0.2 AEP (the 5-year flood) to 55.4 percent for the 0.002 AEP (the 500-year flood).  Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams. ]]></description>
      <pubDate>Tue, 07 Jan 2025 10:28:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487330</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>Investigating the impact of land use disturbance on streamflow regime and sediment connectivity in urban headwater systems</title>
      <link>https://rip.trb.org/View/2442006</link>
      <description><![CDATA[Streamflow regime is defined as the frequency, magnitude, duration, and timing of surface streamflow presence in a stream network. In headwater stream systems—stream reaches that form the start of river networks—streamflow regimes are highly dynamic, helping to maintain ecosystem function in watersheds. Despite their recognized importance, headwater systems are currently classified as “vulnerable,” meaning that they are particularly susceptible to degradation and alteration. Transportation systems often are the impetus of such vulnerability, especially in urban settings, which results from hydraulic alteration to stream channels, increased runoff from impervious areas, and modification to lateral connectivity of the stream to its hillslopes by the transportation network. The transport of non-point source pollutants is intertwined with and controlled by flow regimes in headwater systems, and is often elevated by urban expansion. A particular pollutant of concern in urban headwater systems is elevated fine sediment levels, which have been recognized as one of the most common nonpoint source pollutants of surface waters in the United States (US EPA). 

The overall goal of this project is to investigate how urban landscape disturbance impacts streamflow regime and sediment connectivity in headwater systems by coupling flow state sensing technology, sediment fingerprinting, and model simulations. The project has three primary objectives. The first objective is to quantify the frequency, magnitude, duration, and timing of streamflow regime in urban headwater systems using low-cost flow state data loggers and pressure transducers. The second objective is to utilize sediment fingerprinting methods and end-member unmixing analyses to quantify the contribution of variable landscapes, including landscapes with large degrees of landscape disturbance, to sediment transport in headwater streams. Finally, the third objective is to develop a watershed model to investigate the contribution of headwater streams to watershed sediment budgets. This information will then be leveraged to assess the impact of landscape disturbance on streamflow regime and sediment transport in urban headwater systems. This addresses a need to quantify streamflow permanence in headwater systems and the contribution of headwaters to sediment yield in urban watersheds. Simultaneously, the project will advance methods to characterize hydrologic and sediment connectivity in watersheds. 
The Middle Fork of Beargrass Creek, located within Louisville, Kentucky, will be the testbed to evaluate the impacts of landscape disturbance on streamflow regime and sediment transport. 84% of the Middle Fork of Beargrass Creek is classified as “developed”, and a federal consent decree to reduce combined sewer overflows in Beargrass Creek is currently enacted. 

US DOT Priorities: This project directly supports the US DOT strategic goal related to climate and sustainability. Sediment and soil are of the largest stores of carbon on earth, and the process of eroding and transporting sediment and soil can transform stored carbon into carbon dioxide that contributes to climate change. Transportation systems contribute to increased runoff and sediment transport in urban settings due to hydraulic alteration of stream channels, increased runoff from impervious areas, and modification to lateral connectivity of the stream to its hillslopes by the transportation network, thereby increasing rates of sediment transport in the system. By better understanding the specific role of landscape disturbance, such as through the development of transportation networks, on streamflow regimes and sediment transport, we can better design solutions to mitigate its impact.

Outputs: After the project’s completion, the research team anticipates the following datasets will be procured: (1) approximately two years of streamflow regime data in several headwater tributaries; (2) approximately two years of sediment fingerprinting data in several headwater tributaries and at the watershed outlet; and (3) data to quantify headwater streamflow regime and sediment transport realized in urban headwaters. The project will result in a minimum of three manuscripts. Manuscript 1: which details the impact of urbanization and land use change on streamflow regime in headwater systems. Manuscript 2: which characterizes sediment transport and sediment sources in headwater systems impacted by urbanization and land use change. Manuscript 3: which investigates the controls of streamflow regime and sediment transport in headwater systems as well as the impact of landscape disturbance on streamflow regime and sediment transport in headwater systems. 

Outcomes/Impacts: This proposal emphasizes the critical and timely importance of an improved understanding of the functioning of streamflow permanence and sediment transport in urban headwater systems given that: (1) biodiversity in Kentucky (and throughout the US) is reliant upon high-quality streamflow in headwater systems; (2) such streams are at increased risk of degradation and water quality impairment from urbanization and climate change, and (3) the protection of headwater streams is now reduced under the Clean Water Act after recent changes to the WOTUS rule described by the US Supreme Court. Specifically, results of this study could have timely implications for regulatory agencies who have been tasked with redefining WOTUS (i.e., the US EPA and the USACE). An improved understanding of the functioning of headwater systems is expected to better inform such policy.
Additionally, the proposed project offers several opportunities for undergraduate and graduate students to receive training on use of state-of-the-art and novel technologies to monitor and model streamflow and sediment transport. Students involved in this project will be on the forefront of developing methods to evaluate spatially distributed hydrologic models and will receive advanced training in use of water quality sensing technology, sediment fingerprinting, and model application. Such skills may serve students well as consultants and governmental agencies begin adopting such technologies. 
The team expects that this study will be highly valuable to organizations such as the USEPA and USACE when evaluating improvements in water quality, especially with respect to the currently enacted consent decree in Jefferson County, KY for combined sewer overflows. Furthermore, the team expects that their classifications of the provenance of non-point source pollutants will aid in developing future guidelines for the Louisville Metropolitan Sewer District with respect to sedimentation from new developments. 
]]></description>
      <pubDate>Thu, 17 Oct 2024 10:25:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2442006</guid>
    </item>
    <item>
      <title>Predicting downstream impacts of post-fire sediment inputs to transportation assets over management time scales</title>
      <link>https://rip.trb.org/View/2431162</link>
      <description><![CDATA[This project will develop user-friendly models and geospatial tools to predict secondary, routed impacts to critical infrastructure (i.e., depth and rate of sediment erosion/deposition) caused by the natural down-stream transport of wildfire-derived sediment inputs over management relevant time-scales. The primary objectives are to: (1) develop Machine Learning models to predict post-fire streamflow changes and post-fire burn severity, and then (2) predict potential downstream risks to critical transportation infrastructure and aquatic habitat over time. The resulting geospatial toolkit and risk assessments for selected burned and unburned watersheds will help Colorado Department of Transportation (CDOT) mitigate damages associated with recent wildfires and prioritize long-term infrastructure planning and design in high-risk watersheds. ]]></description>
      <pubDate>Mon, 16 Sep 2024 08:44:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431162</guid>
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    <item>
      <title>Flood Assessment System for TxDOT (FAST)</title>
      <link>https://rip.trb.org/View/2359105</link>
      <description><![CDATA[The Texas Department of Transportation (TxDOT) wishes to move from a reactive to a proactive response during flood emergency operations. Real-time flood map services provide valuable information for TxDOT flood decision making. The National Weather Service initiated the operation of real-time flood inundation maps for Texas in October 2023. The research team will create a Flood Assessment System for TxDOT as an additional set of real-time flood maps to describe flood impact on the road and bridge system. These maps will be distributed to TxDOT's Maintenance Division staff as web services and tested in large scale flood emergency response exercises conducted with TxDOT Districts. The research team will operate and maintain 80 RQ-30 stream gages to support flood forecasting and decision making. Researchers will refine the targeted approach for RQ-30 velocity sensor calibrations to support timely rating development using velocimetry. As many of the 80 RQ-30 gauges as possible will be added to the Interagency Flood Risk Management (InFRM) Flood Decision Support Toolbox. Combining novel gauging techniques with inundation mapping provides real-time streamflow information and transportation flood impacts that enable scenario planning and proactive actions to flood events. This project will be a continuation of Project 0-7095 "Evaluating Improved Streamflow Measurement at TxDOT Bridges."]]></description>
      <pubDate>Mon, 25 Mar 2024 10:40:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2359105</guid>
    </item>
    <item>
      <title>Water quality monitoring network to assess downstream efficacy of green infrastructure and provenance of non-point source pollutants</title>
      <link>https://rip.trb.org/View/2264209</link>
      <description><![CDATA[The research team proposes to establish a water quantity and water quality monitoring network that can be leveraged to evaluate the efficacy of green infrastructure to reduce runoff volumes and identify the provenance of non-point source pollutants in downstream water bodies.
In urban settings, rivers and streams are frequently afflicted by the so-called “urban stream syndrome,” which results from hydraulic alteration of stream channels, increased runoff from impervious areas, non-point source pollution, and modification to the lateral connectivity of the stream to its hillslopes. Urban streams are frequently classified as “flashy,” meaning that transport of water, sediment, other non-point source pollutants occur in brief, yet powerful pulses. This results in a stream system that simultaneously delivers increased discharge and non-point source pollutants during storms, but rapidly dries during recession periods. This has implications for both freshwater ecosystems and water-related infrastructure. 

One approach to combat the urban stream syndrome includes the application of green infrastructure in disturbed landscapes and investigation of the provenance of non-point source pollutants. To evaluate the performance of green infrastructure, extensive in situ monitoring equipment is commonly used on site. While such monitoring indicates that green infrastructure indeed improves on-site water quantity and water quality, a pressing need exists to evaluate the efficacy of green infrastructure to mediate water quantity (including streamflow permanence) and water quality in downstream waterways. Furthermore, the extent to which the effects of green infrastructure perpetuate to downstream water bodies is currently unknown.
The team proposes to establish a water quality and water quantity monitoring network to evaluate the downstream impacts of green infrastructure on water bodies and identify the provenance of non-point source pollutants. The monitoring network will consist of state-of-the-art, multi-parameter water quality and water quantity platforms. Parameters monitored at the platforms will include discharge, pH, dissolved oxygen, conductivity, temperature, turbidity, NO3-, and streamflow presence/absence. Readings will be recorded every 15-minutes. 
The Middle Fork of Beargrass Creek, located within Louisville, KY, will be the testbed to evaluate downstream impacts of green infrastructure. 84% of the Middle Fork of Beargrass Creek is classified as “developed”, and a federal consent decree to reduce combined sewer overflows in Beargrass Creek is currently enacted.
]]></description>
      <pubDate>Fri, 06 Oct 2023 19:11:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2264209</guid>
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    <item>
      <title>A Hydrologic Modeling Framework for Assessing Future Riverine Flood Risk of Critical Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2255635</link>
      <description><![CDATA[The primary goal of this proposal is to develop a high-resolution distributed hydrologic model for the state of New Jersey. The model will provide space-time information of streamflow during flood events and will be calibrated/validated against United States Geological Survey (USGS) streamflow stations. The calibrated model will run for various Global Climate Model scenarios to simulate flood response in the future. Analysis of future flood simulations will be carried out to identify “hot spots” of future riverine flood risk.

The intended outcome of the project is a distributed hydrologic model setup for the state of New Jersey, which will be used for several other studies/applications related to flood warning and flood risk analysis.]]></description>
      <pubDate>Mon, 25 Sep 2023 18:09:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2255635</guid>
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    <item>
      <title>Statewide StreamStats Web Tool for Estimating Streamflow Statistics - Regression Equation Update</title>
      <link>https://rip.trb.org/View/1942650</link>
      <description><![CDATA[Knowledge and understanding of streamflow paths, expected flows and predicted extreme flows during droughts and floods for drainageways in our state is fundamental to habitat and wildlife preservation, and water supply and wastewater discharge. Nebraska's water resources are crucial to the livelihood and survival of its citizens and wildlife, therefore combined effmis of Nebraska's residents, agricultural community, private enterprise and local, state and federal government agencies are essential to the preservation and protection of these water resources. This project focuses on the implementation of StreamStats, a nationwide effort by USGS to use streamgage data, topography and regression equations to provide flow data to the general public for user-selected drainageways. The web-based platform will delineate drainage basins and compute watershed characteristics at user-selected gaged or ungaged stream locations. This web tool will be used by local, state and federal government agencies and private consultants for floodplain management, wetland restoration and mitigation projects, and in the analysis and design of culverts, bridges, stormwater treatment facilities, detention ponds, aquatic organism passage structures and other water resources projects. To implement StreamStats in Nebraska, the geographic information system framework for the web-implementation needs to be built and gage information will be analyzed. .   

]]></description>
      <pubDate>Tue, 21 Jun 2022 13:02:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942650</guid>
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    <item>
      <title>Stream Flow Turbidity Monitoring during Construction</title>
      <link>https://rip.trb.org/View/1895366</link>
      <description><![CDATA[Any in-stream construction work requires permits from US Fish and Wildlife Service. The permitting is based on assumptions of turbidity extent and intensity, which impacts fish health and survival. The permitting restricts us to in stream work so having better quality turbidity data should open work windows, since current assumptions are very likely conservative. This is based on our experience.
Currently the study team assumes that turbidity could go 1000 feet downstream from cofferdam placement and other activities at levels that could cause harm to fish.  There is not a good body of literature to understand and support these assumptions.  If the study team can show that the effects are lesser in extent and severity, it will help us angle for more flexible in water work windows.  
Turbidity data collection supports the Maine Department of Transportation (MaineDOT) and the U.S. Fish and Wildlife Service programmatic agreement. Over the past two years, MaineDOT has hired Stantec to establish baseline data and determine future turbidity limits related to in-water construction events and their effects on Atlantic salmon (Salmo salar) and its critical habitat protected under the Endangered Species Act. This included turbidity data collection at two sites with in-water construction in 2020 and four project sites in 2021. 
The tasks in above mentioned work include establishing monitoring points prior to construction, collecting pre-construction (baseline) and syn-construction (during construction) turbidity samples, reviewing laboratory results, and providing a summary report for each project site. The water sample data collection will be performed as described in Appendix C and D of the “User’s Guide for the Maine Atlantic Salmon Programmatic Consultation (MAP) Version 1.0, March 2017”. 
The study team has developed turbidity monitoring protocols and recently hired Stantec to collect measurements. The study team has a protocol to follow to determine turbidity levels and there’s solid research on extent and severity that can cause harm to fish. However, turbidity is very specific to the location and stream bed composition among other things. More data collection at sites with in stream work is required and a comprehensive analysis of the data before impactful results (more flexible in stream work windows) can be determined.
]]></description>
      <pubDate>Fri, 03 Dec 2021 12:42:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/1895366</guid>
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    <item>
      <title>A Machine Learning-Based System for Predicting Peak Flowrates of Nebraska Streams</title>
      <link>https://rip.trb.org/View/1868902</link>
      <description><![CDATA[In this proposed research, the research team will model peak flowrates in Nebraska streams using new high-resolution datasets and a suite of machine learning algorithms. The team will use data from remote sensing and in-situ sources and study a wide range of predictors. The output would be a state-of-the-art system to estimate peak flowrates, which will be used in flood modeling. The team will build the system in such a way that it can be updated easily in light of new data.]]></description>
      <pubDate>Tue, 03 Aug 2021 23:26:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868902</guid>
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    <item>
      <title>SC StreamStats Phase II: Additional Tools and Layers for Enhanced Workflow and Efficiency</title>
      <link>https://rip.trb.org/View/1742781</link>
      <description><![CDATA[The objective of Phase II of the U.S. Geological Survey (USGS) StreamStats is to develop and incorporate additional tools and functionality into the current (2019) application.  Added functionality could include a storm runoff model using the South Carolina synthetic hydrograph method, hydrographs for gaging statistics, flow-regulation information, and multipoint delineation for basins.  Additional data layers can be developed for such things as rainfall distribution, hydrologic regions, and hydrologic soil groups as well as an update with the National Land Cover Database 2016 data for basin characteristics.  Furthermore, methods for automating lidar updates can be implemented to process and deliver the latest data into StreamStats in a more cost-effective manner.  Many of the functions will provide tools for the South Carolina Department of Transportation and other engineers and water-resource planners which are outside of the scope of the National StreamStats application.]]></description>
      <pubDate>Mon, 05 Oct 2020 09:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1742781</guid>
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    <item>
      <title>Incorporating Snow Processes in the Iowa Flood Information System (IFIS) and Evaluating its Applicability for Nebraska</title>
      <link>https://rip.trb.org/View/1685127</link>
      <description><![CDATA[Accurate and early information on floods are crucial to ensure transportation safety from flood risk. Bridges and roads can be closed in advance as needed if floods are predicted with sufficient lead time. The Iowa Flood Information System (IFIS) is a state-of-the-art flood forecasting system developed at the Iowa Flood Center (IFC) of the University of Iowa. The platform is based on a rainfall-runoff model and uses data from a wide variety of sources. It monitors floods and also calculates five-day flood risk based on the event return period and duration for over 1000 communities in Iowa. The current system, however, does not account for snow-related processes, which is an important factor for Nebraska. Snowmelt, rain-on-snow events, and frozen soil have significant contributions to the overall flood risk in the state. In the proposed research, the research team plans to improve the hydrologic component of the IFIS by accounting for snow. The team will test different parameterizations for snowmelt and select the one that maintains a good trade-off between performance and complexity. The improved model will be rigorously tested and validated using ground observations. Alongside, the team will be installing automated stream sensors on some selected bridges in the region. The sensors are crucial for real-time monitoring of streamflow. Information collected from these sensors will also be assimilated into the model to improve its performance. The team has chosen the Lower Elkhorn Natural Resources District (LENRD) region for conducting this study because snow-driven flooding is common in this region (e.g. Spring 2019 flood) and also because it is close to Iowa, which simplifies the logistics of this project. Finally, the potential of the platform to be applied across the entire state of Nebraska will be evaluated.]]></description>
      <pubDate>Mon, 21 Sep 2020 18:42:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/1685127</guid>
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      <title>SPR-4430:  Multiple Water Course Joint Probability Analysis Procedure Development for Indiana Specific Watersheds</title>
      <link>https://rip.trb.org/View/1653582</link>
      <description><![CDATA[Design of hydraulic structures near a stream confluence requires the use of joint probability of flow exceedances. This project will develop joint probabilities based on Indiana streams for multiple probabilities including 1%, 2%, 4% and 10%. This project will deliver a report detailing the scope of the study, methodology and results related to development of joint probabilities and a recommended procedure on how the newly developed joint probabilities can be implemented in Indiana Department of Transportation (INDOT) projects.
]]></description>
      <pubDate>Wed, 25 Sep 2019 11:05:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/1653582</guid>
    </item>
    <item>
      <title>Real-Time Flood Forecasting for River Crossings - Phase I</title>
      <link>https://rip.trb.org/View/1505631</link>
      <description><![CDATA[The principal investigators (PIs) propose to develop a generic prototype of a flood-forecasting model that is easy to implement at many locations around the Midwest to provide monitoring and forecasting flood potential at critical infrastructure points, such as bridges, where streamflow gauges are not available.  They will develop a real-time web-based visualization platform to display the model predictions.  The platform will display the river network upstream from a point of interest and a time control slider that will allow exploring the evolution of flows everywhere in the network over the past several days and about a week into the future. 

]]></description>
      <pubDate>Wed, 21 Mar 2018 21:27:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1505631</guid>
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