19 Aug 2025

Do Land Models Miss Key Soil Hydrological Processes Controlling Soil Moisture Memory?

Abstract: Soil moisture memory is critical for understanding climatic, hydrological, and ecosystem interactions. Most land surface models overestimate surface soil moisture and its persistency, sustaining spuriously large soil surface evaporation during dry-down periods. Do LSMs miss or misrepresent key hydrological processes controlling SMM? We used Noah-MP with advanced hydrology that represents preferential flow and surface ponding and provides optional schemes of soil hydraulics. Effects were tested, which are generally missed by LSMs in SMM. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive L3 soil moisture and in situ measurements from the International Soil Moisture Network between 2015 to 2019 over the contiguous US. Results suggest soil hydraulics plays a dominant role and the Van Genuchten hydraulic scheme reduces overestimation of the long-term surface SMM produced by the Brooks–Corey scheme; explicitly representing surface ponding enhances SMM for the surface layer and the root zone; and representing preferential flow improves overall representation of soil moisture dynamics. The combination of these missing schemes can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs. LSMs for use in seasonal-to-subseasonal climate prediction should, at least, adopt the Van Genuchten hydraulic scheme.

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.