Skip to main content

PNNL

  • About
  • News & Media
  • Careers
  • Events
  • Research
    • Scientific Discovery
      • Biology
        • Human Health
        • Integrative Omics
        • Microbiome Science
      • Chemistry
        • Catalysis
        • Chemical Physics
      • Computational Research
        • Artificial Intelligence
        • Computational Mathematics & Statistics
        • Graph and Data Analytics
        • High-Performance Computing
        • Software Engineering
        • Visual Analytics
      • Earth System Science
        • Plant Science
        • Atmospheric Science
        • Terrestrial Aquatics
        • Subsurface Science
        • Ecosystem Science
        • Coastal Science
      • Materials Science
        • Solid Phase Processing
        • Science of Interfaces
        • Precision Materials by Design
        • Materials in Extreme Environments
      • Nuclear & Particle Physics
        • Dark Matter
        • Neutrino Physics
        • Flavor Physics
        • Fusion Energy Science
      • Quantum Information Science
    • Energy Resiliency
      • Electric Grid Modernization
        • Distribution
        • Transmission
        • Grid Architecture
        • Grid Analytics
          • AGM Program
        • Grid Cybersecurity
        • Emergency Response
      • Energy Efficiency
        • Building Technologies
          • Building-Grid Integration
          • Advanced Lighting
        • Residential Buildings
          • Energy Efficient Technology Integration
          • Home Energy Score
          • Building America Solution Center
        • Commercial Buildings
        • Federal Buildings
          • Federal Performance Optimization
          • Resilience and Security
        • Building Energy Codes
        • Appliance and Equipment Standards
      • Energy Storage
        • Grid Energy Storage
        • Vehicle Energy Storage
      • Environmental Management
        • Environmental Remediation
        • Waste Processing
        • Radiation Measurement
      • Fossil Energy
        • Subsurface Energy Systems
        • Advanced Hydrocarbon Conversion
      • Nuclear Energy
        • Reactor Licensing
        • Reactor Operations
        • Fuel Cycle Research
        • Advanced Reactors
      • Renewable Energy
        • Hydropower
          • Environmental Performance of Hydropower
          • Hydropower and the Electric Grid
          • Hydropower Cybersecurity and Digitalization
          • Materials Science for Hydropower
          • Water + Hydropower Planning
        • Marine Energy
          • Environmental Monitoring for Marine Energy
          • Marine Biofouling and Corrosion
          • Marine Energy Resource Characterization
          • Testing for Marine Energy
          • The Blue Economy
        • Wind Energy
          • Distributed Wind
          • Offshore Wind
          • Uncertainty Quantification
          • Wildlife and Wind
          • Wind Data Archive and Portal
          • Wind Resource Characterization
        • Geothermal Energy
        • Solar Energy
      • Transportation
        • Vehicle Technologies
          • Emission Control
          • Energy-Efficient Mobility Systems
          • Lightweight Materials
          • Vehicle Electrification
        • Bioenergy Technologies
          • Algal Biofuels
          • Aviation Biofuels
          • Waste-to-Energy and Products
        • Hydrogen & Fuel Cells
    • National Security
      • Computing & Analytics
        • Artificial Intelligence
        • Computational Mathematics & Statistics
        • Graph and Data Analytics
        • High-Performance Computing
        • Software Engineering
        • Visual Analytics
      • Cybersecurity
        • Discovery and Insight
        • Proactive Defense
        • Trusted Systems
      • Nuclear Nonproliferation
        • Stakeholder Engagement
        • Technical Training
      • Weapons of Mass Effect
        • Explosives Detection
        • Chemical & Biological Signatures Science
        • Radiological & Nuclear Detection
    • Lab Objectives
    • Publications & Reports
    • S&T Capabilities
  • People
    • Inventors
    • Diversity
    • Lab Leadership
    • Lab Fellows
    • Staff Accomplishments
  • Partner with PNNL
    • Academia
      • Distinguished Graduate Research Programs
      • Internships
      • Visiting Faculty Program
      • Joint Appointments
      • Joint Institutes
    • Community
      • STEM Education
      • Philanthropy
      • Volunteering
      • Economic Impact
    • Industry
      • Industry Partnerships
      • Licensing & Technology Transfer
      • Entrepreneurial Leave
  • Facilities & Centers
    • All Facilities
      • Atmospheric Radiation Measurement User Facility
      • Bioproducts, Sciences, and Engineering Lab
      • Environmental Molecular Sciences Laboratory
      • Institute for Integrated Catalysis
      • Marine and Coastal Research Laboratory
      • Radiochemical Processing Laboratory
      • Shallow Underground Laboratory
      • Systems Engineering Building
      • Wasteform Development Laboratory
      • PNNL Seattle Research Center
      • PNNL 5G Innovation Studio

Distributed Hydrology Soil Vegetation Model

  • FAQ
  • Tutorials and Datasets
    • Tutorial for DHSVM 2.0
    • Tutorial for DHSVM 3.0
    • Tutorial for DHSVM 3.1
  • Source Code
  • Data Products
  • Documentation
    • Tools
    • Model Operation
    • Model Input Files
    • Processing of Input Files
    • Model Output
  • Publications

Model Operation

Describes how to compile the source code, input requirements, and miscellaneous issues related to running the model.

  • Hardware requirements
  • Compiling and running the source code
  • Input requirements

Hardware requirements

The hardware requirements for DHSVM depend on the size of the application, that is, the number of pixels within the watershed. However, most modern workstations with a large amount of memory (512Mb RAM or more) and a fast processor (Pentium IV, PowerPC G4, or equivalent) will be amply sufficient.

DHSVM does not make use of multiple processors, so a fast single processor machine will result in a shorter runtime than a slower multi-processor machine (assuming no other processes are active)

Compiling and running the source code

DHSVM is written in ANSI-C, and will run under most operating systems. It has been successfully implemented on Pentium PC based systems (under FreeBSD, Linux, and Microsoft Windows), Apple PowerPC based systems (OS X), SUN workstations, and HP workstations.

The Gnu C-Compiler (gcc) is available for all these platforms and operating systems and is the compiler of choice. This means that this is the only compiler used in testing the model codes, and no attempts will be made to support other compilers.

You will need to modify the makefile that comes with the DHSVM source code to reflect the location of the necessary header and library files on your system. DHSVM has an option to display its output in an X-window while the model is running. This requires the availability of the X11 library on your system. To compile the code for the X-window display, uncomment the line in the makefile with DEFS=-DHAVE_X11. 

Similarly, DHSVM can read and write NetCDF files. This option is only available if you have the netcdf libraries on your system. To download the netcdf software and obtain more information, visit the Unidata website. To compile the code for reading and writing NetCDF files, uncomment the line in the makefile with DEFS=-DHAVE_NETCDF.

Input requirements

As a spatially distributed hydrological model, DHSVM is input intensive. In broad terms, the following input is needed for the implementation of the model in a specific area:

  • Digital Elevation Model (DEM) of the basin
  • Soil textural and hydraulic information
  • Vegetation information
  • Meteorological conditions at a subdaily timestep, in particular precipitation, air temperature, humidity, wind speed, incoming shortwave radiation and incoming longwave radiation
  • Information about the stream and road network (location, width, etc.)

Soil and vegetation information is needed at the same resolution as the resolution at which the model is implemented. For each pixel a soil and vegetation type is specified, and a lookup table is used to store the associated soil and vegetation properties.

The specific content and format of each of the input files is specified on the Model Input page.

PNNL

  • Get in Touch
    • Contact
    • Careers
    • Doing Business
    • Environmental Reports
    • Security & Privacy
  • Research
    • Scientific Discovery
    • Energy Resiliency
    • National Security
Subscribe to PNNL News
Department of Energy Logo Battelle Logo
Pacific Northwest National Laboratory (PNNL) is managed and operated by Battelle for the Department of Energy
  • YouTube
  • Facebook
  • Twitter
  • Instagram
  • LinkedIn