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Abstract
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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Knowing requires data |
| Series title | Groundwater |
| DOI | 10.1111/gwat.12553 |
| Volume | 55 |
| Issue | 5 |
| Year Published | 2017 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Nevada Water Science Center |
| Description | 4 p. |
| First page | 674 |
| Last page | 677 |
| Google Analytic Metrics | Metrics page |