Numerical groundwater models are important compo-nents of groundwater analyses that are used for makingcritical decisions related to the management of ground-water resources. In this support role, models are oftenconstructed to serve a speciﬁc purpose that is to provideinsights, through simulation, related to a speciﬁc func-tion of a complex aquifer system that cannot be observeddirectly (Anderson et al. 2015).
For any given modeling analysis, several modelinput datasets must be prepared. Herein, the datasetsrequired to simulate the historical conditions are referredto as the calibration model, and the datasets requiredto simulate the model’s purpose are referred to as theforecast model. Future groundwater conditions or otherunobserved aspects of the groundwater system may besimulated by the forecast model—the outputs of interestfrom the forecast model represent the purpose of themodeling analysis. Unfortunately, the forecast model,needed to simulate the purpose of the modeling analysis,is seemingly an afterthought—calibration is where themajority of time and effort are expended and calibrationis usually completed before the forecast model is evenconstructed. Herein, I am proposing a new groundwatermodeling workﬂow, referred to as the “forecast ﬁrst”workﬂow, where the forecast model is constructed at anearlier stage in the modeling analysis and the outputsof interest from the forecast model are evaluated duringsubsequent tasks in the workﬂow.
|Publication Subtype||Journal Article|
|Title||Forecast first: An argument for groundwater modeling in reverse|
|Contributing office(s)||Texas Water Science Center|
|Google Analytic Metrics||Metrics page|