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Knowing requires data

July 24, 2017

Groundwater-flow models are often calibrated using a limited number of observations relative to the unknown inputs required for the model. This is especially true for models that simulate groundwater surface-water interactions. In this case, subsurface temperature sensors can be an efficient means for collecting long-term data that capture the transient nature of physical processes such as seepage losses. Continuous and spatially dense network of diverse observation data can be used to improve knowledge of important physical drivers, conceptualize and calibrate variably saturated groundwater flow models. An example is presented for which the results of such analysis were used to help guide irrigation districts and water management decisions on costly upgrades to conveyance systems to improve water usage, farm productivity and restoration efforts to improve downstream water quality and ecosystems.

Citation Information

Publication Year 2017
Title Knowing requires data
DOI 10.1111/gwat.12553
Authors Ramon C. Naranjo
Publication Type Article
Publication Subtype Journal Article
Series Title Groundwater
Index ID 70189748
Record Source USGS Publications Warehouse
USGS Organization Nevada Water Science Center