13 Jun 2025

User Guidelines on Catchment Post-Wildfire Hydrological Modeling

Abstract: Wildfires significantly alter watershed hydrology by increasing runoff due to reduced infiltration from soil-water repellency. To predict long-term wildfire impacts, a coupled framework was developed to simulate postfire changes in soil hydraulic properties, infiltration, and hydrological response. This framework integrates Wildfire-Induced Soil Hydraulic (WISH) Factors with a Soil-Moisture Threshold (SMT) formulation in the Green and Ampt infiltration model, representing reduced infiltration due to water repellency. Postfire inputs, including burn severity, soil type, and land use, are formatted for the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model to ensure realistic hydrological simulations. The approach was applied to the 41.7 km² Upper Arroyo Seco watershed in northeast Los Angeles County, where 95% of the area was burned during the August 2009 Station Fire. Hydrological simulations effectively captured increased water repellency and excess runoff following postfire rainfall, demonstrating the model’s ability to represent wildfire-induced watershed changes and improve postfire hydrological assessments.

7 Aug 2023

Soil-Moisture Estimation of Root Zone through Vegetation-Index-Based Evapotranspiration-Fraction and Soil-Properties (SERVES) User’s Manual Version 1.0

Purpose: The purpose of this user’s guide is to provide background methods and implementation guidance on the Soil-moisture Estimation of Root Zone through Vegetation-Index-Based Evapotranspiration-Fraction and Soil-Properties (SERVES) model (Pradhan 2019).

2 Sep 2021

Spatial and Temporal Variance of Soil and Meteorological Properties Affecting Sensor Performance—Phase 2

ABSTRACT: An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of interest. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.

20 Aug 2020

PUBLICATION NOTICE: Spatial and Temporal Variance in the Thermal Response of Buried Objects

ABSTRACT:  Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center’s Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness.

31 Mar 2020

Draft programmatic environmental assessment on snow and soil moisture monitoring network in the Upper Missouri River Basin available for comment

A draft programmatic environmental assessment (PEA) for updating and constructing new soil moisture and plains snowpack monitoring stations in the Upper Missouri River basin is currently available for public comment. Comments must be postmarked or received no later than May 1, 2020.

6 Feb 2015

Reservoir system prepared for 2015 runoff season; previously stored flood waters evacuated

Monthly Missouri River Water Management Report for February 2015. Updates on weather conditions, snow pack, reservoir levels and release forecast.

12 Jan 2015

Corps increases releases due to cold temperatures; normal runoff forecast for 2015

The U.S Army Corps of Engineers Missouri River Water Management Office increased releases from Gavins Point Dam from 17,000 to 20,000 cubic feet per second in late December and early January to offset water lost to ice formation in response to forecasts of cold temperatures.