Calibrating and automating recharge estimates into the Edwards Aquifer, and uncertainty analysis using the Python scripting language and a Monte Carlo sampling technique
The USGS publishes annual estimates of recharge to the Edwards aquifer by using a mass water balance method. The current (2017) methodology relies on a hand-drawn base-flow separation technique to obtain components of the stream hydrograph such as base flow and storm runoff. These components are the...
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| Médium: | Dissertation |
| Jazyk: | angličtina |
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ProQuest Dissertations & Theses
01.01.2017
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| ISBN: | 1369776136, 9781369776133 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | The USGS publishes annual estimates of recharge to the Edwards aquifer by using a mass water balance method. The current (2017) methodology relies on a hand-drawn base-flow separation technique to obtain components of the stream hydrograph such as base flow and storm runoff. These components are then used in the mass balance equations for estimating recharge in the study area. However, the current method is labor intensive and is subject to a potential lack of consistency between different hydrographers using the method. In contrast porting the method into a computational programming language such as python will automate the process as well as offering more transparency for estimating recharge into the Edwards aquifer. With this method automated in python it is possible to apply uncertainty analysis using tools such as PEST++ and pyEMU. Uncertainty analysis is becoming a more regular push for hydrological modeling as stakeholders require the quantification of uncertainty to help better inform their constituents and for better water resource practices. The first chapter of this document is a description of calibrating a series of python scripts to produce a calibrated model to estimate recharge into the Edwards aquifer based on the USGS’s annually published recharge estimates. The second chapter of this document explores the uncertainty of this method using a Monte Carlo sampling technique and the GLUE (generalized likelihood uncertainty estimation) method for filtering likely outcomes. The results of this study shows that there is a vast range of of uncertainty when pertaining to years of heavier rainfall than years of less rainfall in the study area. This would imply that the current method used by the USGS may only produce accurate recharge estimates for dryer years and a new approach to estimating recharge may need to be explored during years with more precipitation. |
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| Bibliografie: | SourceType-Dissertations & Theses-1 ObjectType-Dissertation/Thesis-1 content type line 12 |
| ISBN: | 1369776136 9781369776133 |