Development and Application of a Soil Moisture Downscaling Method for Mobility Assessment

Abstract

Soil moisture is a critical variable for many Army activities including mobility assessments. Several methods can be used to produce soil moisture patterns at an intermediate resolution (grid cells with ~1 km linear dimension). However, mobility assessments require fine-resolution estimates (~30 m grid cells). Thus, a method is required to downscale intermediate-resolution patterns to finer resolutions. Fortunately, fine-resolution variations in soil moisture are known to depend on various topographic attributes, and topographic data are available at fine-resolutions for almost any region on Earth. The overall objective of this project is to develop, calibrate, and apply a method to estimate fine-scale soil moisture patterns based on intermediate-resolution soil moisture estimates and the observed topographic dependence of soil moisture. The method is a conceptual model that estimates soil moisture values by assuming that the soil moisture pattern is at equilibrium and by inferring the spatial variations of vadose-zone processes from topographic characteristics. The method is calibrated using the Tarrawarra and Cache la Poudre catchments, which have extensive soil moisture datasets. It is then applied to a region of interest in Afghanistan for dry, wet, and very wet scenarios where only intermediate-resolution estimates are available.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2011
Accession Number
ADA547252

Entities

People

  • Jeffrey D. Niemann
  • Michael L. Coleman

Organizations

  • Colorado State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Calibration
  • Department Of Defense
  • Drainage Basins
  • Engineering
  • Environmental Engineering
  • Equations
  • Geography
  • Grids
  • Heat Energy
  • Humidity
  • Improvised Explosive Devices
  • Latent Heat
  • Radiation
  • Remote Sensing
  • Students
  • Time-Domain Reflectometry
  • Topography

Readers

  • Agricultural Chemistry/Soil Science
  • Computer Vision.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference