GMS (software): Difference between revisions

GMS was initially developed by the Engineering Computer Graphics Laboratory at [[Brigham Young University]] (later renamed in September 1998 to ''Environmental Modeling Research Laboratory'' or EMRL)<ref name="ecgl 19990421">{{cite web|url=http://emrl.byu.edu/|title=Environmental Modeling Research Laboratory|publisher=Engineering Computer Graphics Laboratory at [[Brigham Young University]]|accessdate=March 5, 2018|archivedate=April 21, 1999|archiveurl=https://web.archive.org/web/19990421051442/http://emrl.byu.edu/}}</ref> in the late 1980s on [[Unix]] workstations. The development of GMS was funded primarily by The [[United States Army Corps of Engineers]] and was known—until version 4.0<ref name="ssg gms4">{{cite web|url=http://www.scisoftware.com/products/gms_details/gms_details.html|title=GMS 4.0|publisher=Scientific Software Group|accessdate=March 5, 2018|archivedate=March 5, 2018|archiveurl=http://www.webcitation.org/6xhR0ZssD}}</ref>—as the ''[[United States Department of Defense|Department of Defense]] Groundwater Modeling System'' or ''DoD GMS''. It was later [[Porting|ported]] to [[Windows (operating system)|Windows]] platforms in the mid 1990s. Version 3.1 was the last supported version for [[HP-UX]], [[IRIX]], [[OSF/1]], and [[Solaris (operating system)|Solaris]] platforms.

In April 2007, the main software development team at EMRL entered private enterprise as [[Aquaveo]], [[Limited liability company|LLC]], and continue to develop GMS and other software products, such as [[WMS (software)|WMS]] (Watershed Modeling System) and [[SMS (hydrology software)|SMS]] (Surface-water Modeling System).

==Examples of GMS implementation==

*Using GMS, a methodology has been utilized for development of conceptual groundwater flow model in which spatially distributed values for groundwater recharge for Pali Area, India. To accomplish this, “Groundwater Modeling System (GMS), which is a comprehensive groundwater modeling environment, was used with GIS based graphical preprocessing tools to automate and streamline the modeling process” (Signhal and Goyal, 1990). GMS was praised for “seamlessly interfac[ing] with MODFLOW and several other preeminent groundwater models, … provid[ing] advanced graphical features for viewing and calibrating model results” (Signhal and Goyal, 1990). The authors created seven types of coverage using several GMS tools.

*Using GMS, a production model, and a soil model facilitated the development of a “marketable permit scheme that can effectively manage nitrate pollution of groundwater supplies for communities in rural areas without hindering agricultural production in watersheds” (Morgan, Coggins, and Eidman, 249) The cost of drinking water is increasing due to nitrate contamination of domestic and municipal well water supplies. Specifically, “GMS provides an interface to the groundwater flow model, MODFLOW, and the contaminant transport model, MT3D. MODFLOW is a three-dimensional, cell-centered, finite-difference, saturated-flow model capable of both steady-state and transient analyses. … These two models, when put together, provide a comprehensive tool for examining groundwater flow and nitrate transport and accumulation” (Morgan, Coggins, and Eidman, 252). With the use of GMS, “the contamination problem [can be converted] from a nonpoint source to a point-source problem,” thus “illustrating the importance of incorporating tools from other disciplines to initiate new avenues of economic research on the problem of groundwater contamination from agricultural production” (Morgan, Coggins, and Eidman, 258).<ref>{{Citation |last1=Morgan |first1=Cynthia L. |last2=Coggins |first2=Jay S. | last3=Eidman |first3=Vernon R. |title=Tradable Permits for Controlling Nitrates in Groundwater at the Farm Level: A Conceptual Model |journal=Journal of Agricultural and Applied Economics |volume=32 |issue=2 |pages=249–258 |date=August 2000}}</ref>

*The shifts in the San Pedro Basin's water budget over the past century "have been assessed using a modified version of an existing hydrological model of the San Pedro Basin (Goode and Maddock, 2000)" that was developed using GMS. A "three dimensional finite difference transient groundwater-surface water flow model of the study area [was] developed using Groundwater Modeling System (GMS, 2007), a GIS based pre- and post-processor that employs MODFLOW" (Serrat-Capdevila, et al., 57) This model was employed "to simulate the hydrology of the current century, from 2000 to 2100, under different climate scenarios and model estimates" (Serrat-Capdevila, et al., 48). The study's results, based on the GMS-derived model, "provide a basis for the inclusion of representative climate scenarios into the basin's existing decision support system model" (Serrat-Capdevila, et al., 48).<ref>{{Citation |last=Serrat-Capdevila |first=Aleix|title=Modeling climate change impacts--and uncertainty-–on the hydrology of a riparian system: The San Pedro Basin (Arizona/Sonora) |journal=Journal of Hydrology |volume=347 |pages=48–66 |date=2007|display-authors=etal |doi=10.1016/j.jhydrol.2007.08.028}}</ref>

*Many resources such as GMS “may be used to build a conceptual understanding of flow in the [Carbonate aquifers] system, including drilling data, well tests, geophysical surveys, tracer tests, and spring gaging” (Quinn, Tomasko, and Kuiper, 343). For this study, "MODFLOW model construction was facilitated using the Groundwater Modeling System (GMS)" (Quinn, Tomasko, and Kuiper, 348). MODFLOW runs within GMS allowed the automatic calculation of model fluxes. GMS proved useful even though “the drain feature in MODFLOW was originally developed to simulate agricultural drainage tiles that remove water from an aquifer” (Quinn, Tomasko, and Kuiper, 347). With the aid of the GMS-developed MODFLOW model, the authors were able to create "a method of numerically modeling the heterogeneities of flow in a karst environment by assigning sequences of adjacent model cells with drains to simulate conduits" (Quinn, Tomasko, and Kuiper, 349). Hence, "with improved coverage of site data… an interpretive model such as this could evolve into a more effective tool for testing conceptual models, identifying data gaps, assessing water resources, or comparing remediation scenarios."<ref>{{Citation |last1=Quinn |first1=John J. |last2=Tomasko |first2=David |last3=Kuiper |first3=James A. |title=Modeling complex flow in a karst aquifer |journal=Sedimentary Geology |volume=184 |pages=343–351 |date=2006 |doi=10.1016/j.sedgeo.2005.11.009}}</ref>

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