10 Jun 2024

Post-wildfire Curve Number Estimates for the Southern Rocky Mountains in Colorado, USA

Abstract: The curve number method first developed by the USDA Soil Conservation Service (now the Natural Resources Conservation Service) is often used for post-wildfire runoff assessments. These assessments are critical for land and emergency managers making decisions on life and property risks following a wildfire event. Three approaches (i.e., historical event observations, linear regression model, and regression tree model) were used to help estimate a post-wildfire curve number from watershed and wildfire parameters. For the first method, we used runoff events from 102 burned watersheds in Colorado, southern Wyoming, northern New Mexico, and eastern Utah to quantify changes in curve number values from pre- to post-wildfire conditions. The curve number changes from the measured runoff events vary substantially between positive and negative values. The measured curve number changes were then associated with watershed characteristics (e.g., slope, elevation, northness, and eastness) and land cover type to develop prediction models that provide estimates of post-wildfire curve number changes. Finally, we used a regression tree method to demonstrate that accurate predications can be developed using the measured curve number changes from our study domain. These models can be used for future post-wildfire assessments within the region.

14 May 2024

Snow-Impacted National Inventory of Dams by GAGESII Watershed

Abstract: This Engineering Research and Development Center (ERDC) Technical Note describes the development of a set of locations within the contiguous United States (CONUS) where snowmelt is a component of the annual streamflow. The locations are selected from the US Geological Survey (USGS) Geospatial Attributes of Gages for Evaluating Streamflow II (GAGESII) and National Inventory of Dams (NID) data sets. The 30-year normal snow regimes were used to identify all GAGESII watersheds that have any of the basin delineated as transitional (rain/snow), snow dominated, or perennial snow zones. NID dams that are within snow affected GAGESII watersheds are included in the data set. The purpose of this ERDC Technical Note is to describe the development of a comprehensive data set of CONUS GAGESII and dam infrastructure affected by snow changing regimes.

31 Oct 2023

Stage Frequency Analysis from Snowmelt Runoff near Utqiaġvik, Alaska

Abstract: For the village of Utqiaġvik, located at the North Slope of Alaska, a stone-armored revetment along the coastline is proposed to reduce coastal erosion. The inner drainage capacity of the revetment must be sufficient to handle seasonal runoff from snowmelt. For this effort, we investigated the snowmelt runoff and the hydraulic impact at the watershed outlet using numerical snow and hydraulic modeling of the study area. We validated the snow model results by comparing simulated snow water equivalent (SWE) values to field measurements. Additionally, the snow model was validated using satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area (SCA) products and time-lapse camera imagery during snowmelt. Our results indicate that the simulated SWE and snowmelt dates agree closely with measured values. The timing of modeled runoff onset was less accurate due to natural processes that delay snowmelt runoff such as snow dams and refreeze. The effect of the uncertainty from both runoff timing and volume was addressed with a Monte Carlo simulation of stage-frequency curves for the lagoons that receive snowmelt runoff. These stage-frequency curves can be used directly in the design of outlet, drainage or discharge structures for the proposed revetment.

30 Oct 2023

Initial Data Collection from a Fiber-Optic-Based Dam Seepage Monitoring and Detection System

Abstract: Visual inspection is the most used method to detect seepage at dams. Early detection can be difficult with this method, and use of appropriate real time monitoring could significantly increase the chances of recognizing possible failure. Seepages can be identified by analyzing changes in water and soil temperature. Optical fiber placed at the embankment’s downstream toe has been proven to be an efficient means of detecting real time changes at short intervals over several kilometers. This study aims to demonstrate how temperatures measured using fiber optic distributed sensing can be used to monitor seepage at Moose Creek Dam, North Pole, Alaska. The fiber optic cable portion of the monitoring system is installed along a section of the embankment where sand boils have occurred. Though no flood event occurred during this monitoring period, routine pumping tests of nearby relief wells resulted in an increase of soil and water temperature (up to 13°C) along a 100 m section where sand boils were detected during the 2014 flood events. Measurements during a flood event are expected to provide a quantitative assessment of seepage and its rate.

25 May 2023

Mississippi River Climate Model–Based Hydrograph Projections at the Tarbert Landing Location

Abstract: To better understand and prepare for the possible effects associated with potential climate changes on the lower Mississippi River, the State of Louisiana Coastal Protection and Restoration Authority sought information on the historical, current, and projected future hydrodynamics of the Mississippi River. To this end, flow duration curves (FDC) for the Tarbert Landing location were generated, based on climate models derived from two of the four scenarios of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), multimodel ensemble representative concentration pathways (RCPs). The global CMIP5 datasets were used by the variable infiltration capacity land surface model to produce a runoff dataset, using a bias-correction spatial disaggregation approach. The runoff datasets were then applied to simulate streamflow using the Routing Application for Parallel computatIon of Discharge (RAPID) river routing model. Based on the streamflow, FDCs were calculated for 16 CMIP5 as well as observed historical data at the Tarbert Landing location. Key observations from the results are that the 90th percentile exceedance of the simulated versus the observed flows is more frequent for the RCP 8.5 scenario than for the RCP 4.5 scenario and that the maximum annual flows for the RCP 8.5 scenario are generally smaller than for the RCP 4.5 scenario.

30 Nov 2022

Automation of Gridded HEC-HMS Model Development Using Python: Initial Condition Testing and Calibration Applications

Abstract: The US Army Corps of Engineers’s (USACE) Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) rainfall-runoff model is widely used within the research community to develop both event-based and continuous rainfall-runoff models. The soil moisture accounting (SMA) algorithm is commonly used for long-term simulations. Depending on the final model setup, 12 to 18 parameters are needed to characterize the modeled watershed’s canopy, surface, soil, and routing processes, all of which are potential calibration parameters. HEC-HMS includes optimization tools to facilitate model calibration, but only initial conditions (ICs) can be calibrated when using the gridded SMA algorithm. Calibrating a continuous SMA HEC-HMS model is an iterative process that can require hundreds of simulations, a time intensive process requiring automation. HEC-HMS is written in Java and is predominantly run through a graphical user interface (GUI). As such, conducting a long-term gridded SMA calibration is infeasible using the GUI. USACE Construction Engineering Research Laboratory (CERL) has written a workflow that utilizes the existing Jython application programming interface (API) to batch run HEC-HMS simulations with Python. The workflow allows for gridded SMA HEC-HMS model sensitivity and calibration analyses to be conducted in a timely manner.

5 Aug 2021

Changes in Climate and Its Effect on Timing of Snowmelt and Intensity-Duration-Frequency Curves

Abstract: Snow is a critical water resource for much of the U.S. and failure to ac-count for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall in-tensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.

19 Jul 2021

A Pulse of Mercury and Major Ions in Snowmelt Runoff from a Small Arctic Alaska Watershed

Abstract: Atmospheric mercury (Hg) is deposited to Polar Regions during springtime atmospheric mercury depletion events (AMDEs) that require halogens and snow or ice surfaces. The fate of this Hg during and following snowmelt is largely unknown. We measured Hg, major ions, and stable water isotopes from the snowpack through the entire spring melt runoff period for two years. Our small (2.5 ha) watershed is near Barrow (now Utqiaġvik), Alaska. We measured discharge, made 10 000 snow depths, and collected over 100 samples of snow and meltwater for chemical analysis in 2008 and 2009 from the watershed snowpack and ephemeral stream channel. Our results suggest AMDE Hg complexed with Cl− or Br− may be less likely to be photochemically reduced and re-emitted to the atmosphere prior to snowmelt, and we estimate that roughly 25% of the Hg in snowmelt is attributable to AMDEs. Projected Arctic warming, with more open sea ice leads providing halogen sources that promote AMDEs, may provide enhanced Hg deposition, reduced Hg emission and, ultimately, an increase in snowpack and snowmelt runoff Hg concentrations.

12 May 2021

Wintertime Snow and Precipitation Conditions in the Willow Creek Watershed above Ririe Dam, Idaho

ABSTRACT:  The Ririe Dam and Reservoir project is located on Willow Creek near Idaho Falls, Idaho, and is important for flood risk reduction and water supply. The current operating criteria is based on fully storing a large winter runoff event. These winter runoff events are generally from large storm events, termed atmospheric rivers, which produce substantial precipitation. In addition to the precipitation, enhanced runoff is produced due to frozen soil and snowmelt. However, the need for additional water supply by local stakeholders has prompted the U.S. Army Corps of Engineers to seek to better understand the current level of flood risk reduction provided by Ririe Dam and Reservoir.  Flood risk analysis using hydrologic modeling software requires quantification of the probability for all of the hydrometeorologic inputs. Our study develops the precipitation, SWE, and frozen ground probabilities that are required for the hydrologic modeling necessary to quantify the current winter flood risk.

29 Jan 2021

Stormwater Management and Optimization Toolbox

Abstract: As stormwater regulations for hydrologic and water quality control become increasingly stringent, Department of Defense (DoD) facilities are faced with the daunting task of complying with multiple laws and regulations. This often requires facilities to plan, design, and implement structural best management practices (BMPs) to capture, filter, and/or infiltrate runoff—requirements that can be complicated, contradictory, and difficult to plan. This project demonstrated the Stormwater Management Optimization Toolbox (SMOT), a spreadsheet-based tool that effectively analyzes and plans for compliance to the Energy Independence and Security Act (EISA) of 2007 pre-hydrologic conditions through BMP implementation, resulting in potential cost savings by reducing BMP sizes while simultaneously achieving compliance with multiple objectives. SMOT identifies the most cost-effective modeling method based on an installation’s local conditions (soils, rainfall patterns, drainage network, and regulatory requirements). The work first demonstrated that the Model Selection Tool (MST) recommendation accurately results in the minimum BMP cost for 45 facilities of widely varying climatic and regional conditions, and then demonstrated SMOT at two facilities.