eRAMS: Environmental Resources Assessment and Management System

Opportunity

Available for Licensing (copyright)
Collaborations: Research and Business

Inventors

Arabi Mazdak
Luis A Garcia
David Patterson

At A Glance

The eRAMS technology is a comprehensive geographic information system (GIS) that facilitates the assessment, planning and implantation of robust and reliable solutions for natural resource/environmental management, energy development, community/urban development and planning, and other similar problems where distributed decision making and participation is critical.

Licensing Director

Mandana Ashouri
Mandana.Ashouri@colostate.edu
970-491-7100

Reference No.:  10-080

Background

The eRAMS platform is available online as an example
Greater and customized capabilities of the eRAMS system for developers are available for license.

Technology Overview

The eRAMS technology is a participatory geographic information system (GIS) that operates on a web platform and re­quires no software installation by the end users. Users can access all components of the platform on the internet. The tool works across spatial scales from fields to watersheds and daily or larger time steps (e.g., monthly, seasonal, or annual). The tool is fully compatible with other commonly used databases/GIS technologies, and thus, takes advantage of readily available data. The eRAMS platform facilitates integration of complex models with state-of-the-art optimization and deci­sion analysis algorithms to derive most cost-effective solutions for a system of interest. eRAMs enables users to explore the tradeoffs between conflicting socioeconomic and environmental criteria, but more importantly, can unambiguously identify several solutions that are consistent with users’ priorities. the existing capacities of the technology include:

Data Inventory: eRAMS is equipped with a digitization module that facilitates drawing point, line, and/or polygon features on aerial photos and satellite imageries (such as Google Maps) to specify field boundaries or conservation practic­es and enter their attributes. The digitized features are automatically overlaid with data sources such as soils, land use, and elevation for conservation assessment and planning. Specifically, major soil types, land use, and slope of polygons are ex­tracted and stored. Additionally, eRAMS enables the stakeholders to identify their water quality goals for the assess­ment/planning process. Goals might include reducing pollutant loads from a field or at the outlet of the watershed. The stakeholders then select the conservation practices that will be included in the analysis and the costs that are included in the analysis. The economic analysis may be a maximum total budget, or the goal may be to reduce costs to achieve a cer­tain water quality target.

Modeling: An example of models included in the eRAMS technology includes natural resource models that are use around the world. For example, the field-scale water quality benefits of conservation practices are evaluated using the Agricultural Policy/Environmental eXtender (APEX, http://epicapex.brc.tamus.edu) model, while the watershed scale bene­fits are estimated using the Soil and Water Assessment Tool (SWAT, http://swatmodel.tamu.edu/). Both of the models have been extensively examined for conservation assessment and planning. The following data are automatically collected and used to create inputs for APEX, SW AT, and other models and their calibration:

  • Land use: NLCD, NASS, and/or others
  • Soil: SSURGO, STATSGO, and/or others
  • Topography: 30 x 30 meters digital elevation model (DEM) or other resolutions
  • Sub-watersheds: user defined or automatically derived from ArcGlS interface
  • Point source: PCS or other database
  • Climate data: daily temperature, precipitation ( optional: solar radiation and wind speed)
  • Crop and management databases including irrigation practices
  • USGS flow data (for calibration of hydro logic components)
  • Water quality data (for calibration of water quality components)

eRAMS is equipped with sensitivity analysis, uncertainty analysis, and automatic calibration engines that facilitate para­meterization of imbedded models for the area of interest on a parallel computing platform.

Remote Sensing (ReSET) [1]: ReSET is a surface energy balance model that uses satellite imagery with thermal band (landsat 5 and 7) to estimate evapotranspiration for agriculture fields. ReSET has been applied in several agriculture areas in the U.S (South Plate river basin (Colorado), Arkansas river basin (Colorado) and the Palo Verde irrigation district (California)), ReSET provides actual daily ET at the collection date of satellite imagery, the daily ET estimates can then be combined and via time interpolation technique actual seasonal ET can be computed for each single pixel (30m by 30m) in each field. The advantage of estimating daily and seasonal ET using this approach over the traditional crop coefficient ap­proach is that the estimated ET represents the actual amount of water consumed by the crop cultivated in the field that in many cases due to field condition does not match the empirical ET computed using reference ET and crop Kc, this ap­proach provides an important tool for several irrigation management practices, such as identifying irrigation problems and evaluating irrigation efficiency, uniformity and consistency, the impact of varies factors that affects crop yield such as high soil salinity can be also identified by the model, ReSET can also be used as a component in monitoring water rights and detecting unauthorized irrigation practices.

Conservation Practices (BMP) [2]: The BMP module simulates the impact(s) of conservation practices on fate and transport of pollutants. Various processes that are considered when representing a practice include: infiltration; surface runoff (peak and volume); upland erosion (sheet and rill erosion); gully and channel erosion; nutrient and pesticide load­ings from upland areas; and within-channel processes. The representation of practices in eRMAS draws on the technical practice standards from NRCS Field Office Technical Guide (FOTG).

Scenario Analysis: With this module the user can compare various scenarios and evaluate the tradeoffs between costs and environmental benefits of different management decisions. For example, they will be able to compare the per­formance of buffer strips with varying widths, or evaluate the impacts of different fertilizer application rates, timing and methods.

Optimization [3]: The system optimization component of eRAMS enables users to identify desired cost-effective management plans that achieve their environmental objectives at lowest cost. This tool explores the tradeoffs between en­vironmental, economic, and sustainability criteria. The component includes algorithms for solving single objective, multi­objective, and hybrid discrete/continuous optimization problems.

Map Production: eRAMS provides the ability to quickly define map collections for production of map sheets that can facilitate the decision-making process.

Benefits
  • No requirements for hardware/software installation
  • Automated web-based overlay toolboxes, web-based queries based on spatial location and attributes
  • Participatory GIS which allows users to collect, organize and share geospatial data
  • Automatic integration of complex modeling and optimization platforms
  • Decentralized group of users
  • Real-time, satellite-based high-resolution estimation of surface water budget
Applications

The eRAMS technology will serve as a platform for businesses to benefit from vast data resources and model­ing/optimization platforms available in eRAMs. Also, businesses can plug in their own models and benefit from geospatial data collection, organization and analyses available from eRAMS.

References

[1] Elhaddad, A., Garcia, L.A. (2008) “Surface Energy Balance-Based Model for Estimating EvapotranspirationTaking Into Account Spatial Variability in Weather”, ASCE Journal of Irrigation and Drainage- 134(6) 681-689.
[2] Arabi, M., J.R. Frankenberger, B. Engel, and J.G. Arnold (2008). “Representation of agricultural management practices with SWAT.” Hydrological Processes, vol. 22, 3042-3055.
[3] Arabi, M., R.S. Govindaraju, and M.M. Hantush (2006). “Cost-effective allocation of watershed management practices using a genetic algorithm.” Water Resources Research, 42, W10429.

Last updated: January 2020

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