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  • Missouri River upper basin runoff continues below average forecast

    The updated 2025 calendar year runoff forecast for the Missouri River Basin above Sioux City, Iowa, continues to be below average. March runoff in the Missouri River Basin above Sioux City was 2.3 million acre-feet, 76% of average.

  • Ohio Creek Urban Coastal Storm Risk Management Project: An Application of Engineering With Nature® Principles in Practice

    Purpose: The Engineering With Nature® (EWN®) program within the US Army Corps of Engineers (USACE) funds research projects occurring in a myriad of environments, including in marine coasts, freshwater coasts, and fluvial (riverine) systems. Yet there have been fewer projects documented where EWN principles have been applied in urban landscapes, particularly to manage flood risk, a main civil works mission of the USACE. Natural hazards including increased flashiness associated with intense rainfall events have prompted the need for more sustainable infrastructure solutions that reduce flood risks in urban areas, especially when such solutions desired by stakeholders are nature-based solutions. This technical note documents a flood risk management project in Norfolk, Virginia, that incorporates EWN principles in a tidal estuary environment that not only reduces flood risk, but also provides numerous other environmental, social, and economic benefits.

  • Virtual Missouri River water management public meetings set for April

    Two meetings to hear from the public about planned operation of the Missouri River Mainstem System will be held the first week in April.

  • Spring Missouri River public meetings to be virtual

    Virtual meetings on Missouri River Mainstem Reservoir system operations are in the process of being scheduled. Meeting dates and times will be announced as the details are finalized. In-person public meetings on Missouri River Mainstem Reservoir system operations, which were scheduled for March 19, 31 and April 2 have been canceled.

  • Gavins Point releases increasing for navigation flow support; Upper basin runoff stays below average

    The updated 2025 calendar year runoff forecast for the Missouri River Basin above Sioux City, Iowa, continues to be below average. February runoff in the Missouri River Basin above Sioux City was 1.0 million acre-feet, 91% of average. “Runoff into the reservoir system was slightly below average for the month of February and conditions across most of the basin remain dry,” said John Remus, chief of the U.S. Army Corps of Engineers’ Missouri River Basin Water Management Division.

  • Corps begins Phase I floodfight activities along the White River near Des Arc, Arkansas

    The Memphis District, U.S. Army Corps of Engineers (USACE) has begun Phase I floodfight activities along the White River in Arkansas due to high river stages. The area of current flooding is along the White River near Des Arc, Arkansas. During Phase I floodfight activities, USACE personnel deploy to the affected areas and monitor all federal flood control works including levees, flood walls and pumping stations. They will also continue to monitor rainfall amounts in the affected areas, and National Weather Service forecasts to determine if further action is warranted. USACE will deploy additional personnel and resources as required to help ensure the safety of life and property.

  • Below average runoff continues for upper Missouri River Basin in 2025

    The updated 2025 calendar year runoff forecast for the Missouri River Basin above Sioux City, Iowa, continues to be below average. January runoff in the Missouri River Basin above Sioux City was 0.7 million acre-feet, 92% of average. Runoff was near or below average for most of the Missouri River Basin, and most of the upper basin had below-normal precipitation.

  • Engineering With Nature: Natural Infrastructure for Mission Readiness at U.S. Navy and Marine Corps Installations

    Abstract: This book illustrates some of the current challenges and hazards experienced by military installations, and the content highlights activities at eight U.S. Navy and Marine Corps military installations to achieve increased resilience through natural infrastructure.

  • Below-average runoff and reservoir storage expected for the Missouri River Mainstem System in 2025

    For the 2024 calendar year, Missouri River basin runoff above Sioux City, Iowa totaled 23.3 million acre-feet, 91% of average. Dry conditions continue to affect the upper Missouri River Basin at the start of the 2025 calendar year, so the U.S. Army Corps of Engineers is forecasting below-average runoff into the mainstem reservoir system. For 2025, runoff in the Missouri River basin above Sioux City, Iowa is forecast to be 20.2 MAF, 79% of average.

  • Numerical Modeling of Supercritical Flow in the Los Angeles River: Part II: Existing Conditions Adaptive Hydraulics Numerical Model Study

    Abstract: The Los Angeles District of the US Army Corps of Engineers is assisting the City of Los Angeles with restoration efforts on the Los Angeles River. The city wishes to restore portions of the channelized river to a more natural state with riparian green spaces for both wildlife and public recreation usage. The Los Angeles River provides an important role from a flood-control perspective, and functionality needs to be preserved when contemplating system modifications. This report details the development of an Adaptive Hydraulics numerical model capable of modeling this complex system consisting of both subcritical and supercritical flow regimes. The model geometry was developed to represent the existing conditions system for future usage in quantifying the impact associated with proposed restoration alternatives. Due to limited hydraulic data in the study area, an extensive model validation to observed data was not possible. A model was developed and simulated using the most appropriate input parameters. Given the lack of measured data for model validation, an extensive number of sensitivity simulations were completed to identify the most impactful parameters and quantify a reasonable level of confidence in the model results based on the uncertainty in the model inputs.