In the formula “How is the spatial pattern of A related to the spatial pattern of B via mechanism C?”, my research question for this class is made of the following parts:

• A: ion and isotope concentrations in wells tapping the basalt aquifer
• B: mapped faults
• C: groundwater flow as determined by hydraulic conductivity of the geologic setting

This all adds up into:

“How is the spatial pattern of ion and isotope concentrations in wells tapping the basalt aquifer related to the spatial pattern of mapped faults via the mechanism of groundwater flow as determined by hydraulic conductivity of the geologic setting?”

This question might have thrown you, the reader, into the figurative deep end! For context, I’m studying the basalt aquifer in the Oregon portion of the Walla Walla Basin. This is located in north eastern Oregon, about thirty minutes northeast of Pendleton.

Figure 1: Walla Walla Sub-Basin Location

The wells that I am studying are drilled into the three most extensive formations within the Columbia River Basalts that are present in my study area: the shallow Saddle Mountains layer, the intermediate Wanapum Basalt , and the deepest Grande Ronde Basalt. The wells and faults that I am studying are predominantly on the transition area between the “lowland” areas where the basalts are covered by layers of sediment deposited in the ~15 million years since they were deposited and the upland areas where the basalt is exposed at the surface.

Figure 2: Geologic map of the study area

Wells in this area are between 300 and 1,200 feet in depth, and primarily serve irrigation and municipal uses. Over the past 50 years there has been a noticeable downward trend in groundwater elevations in many of the wells. My research is part of a larger Oregon Water Resource Department project that seeks to better understand this groundwater system, faults and all. Faults add an element of the unknown to models of groundwater flow unless they are specifically studied, as they can formed either barrier or pathways for groundwater flow depending on their structures and characteristics. Faults with low hydraulic conductivity can block or significantly slow groundwater flow, while faults with higher hydraulic conductivity allow water to flow through them more easily. My research uses ion chemistry and isotope concentrations to characterize the path that groundwater has taken through the subsurface into the well.

Datasets:

A: In my research I have analytical data for 32 wells, whose XY locations were determined by field confirmation of my collaborators’ well log database. As groundwater is a 3D system, I have to consider Z values as well. The well depths and lithology information is also from my collaborators’ database, and was based on information of varying quality recorded at the time that the well was drilled. My analytical data provides a snapshot of water chemistry during the summer of 2018. I have only one temporal data point per well. At all 32 wells, I collected samples to be analyzed for pH, temperature, specific conductivity, oxygen isotopes 16 and 18, and hydrogen isotopes 1 and 2. At a subset of 18 of those wells I collected additional samples for tritium, carbon 14, and major ion analysis.

B: The shapefile of faults mapped at the surface was created by Madin and Geitgey of the USGS in their 2007 publication on the geology of the Umatilla Basin. There is some uncertainty in my analysis as far as extending this surface information into the subsurface. USGS studies have constrained proposed ranges of dip angles for the families of faults that I am studying, but not exact angles for any single mapped fault.

Figure 3: locations of wells that were sampled mapped with mapped fault locations.

Hypotheses:

Where faults act as barriers, I hypothesize that parameter values will differ in groups on either side of a fault. Specifically, a barrier fault might cause older, warmer water to rise into upper aquifer layers, and the downstream well might show a signature of more local recharge.

Where faults act as conduits, I hypothesize that water chemistry and isotopes of samples from wells on either side of the fault would indicate a relatively direct flowpath from the upstream well to a downstream well. Over a short distance, this means that ion and isotope concentrations would not differ significantly in wells across the fault.

Approaches:

I would like to use principal component analysis to identify grouping trends of the samples, and then map the results. Additionally, a bivariate comparison of wells on either side of the fault could be interesting? I would like to find some way to bring in distance from a fault into the model too.

Expected outcome: My output would be a mixture of statistical relationships and maps of those relationships.

Significance.  How is your spatial problem important to science? to resource managers?

The Walla Walla Subbasin’s basalt aquifers have recently been deemed over-allocated by the Oregon Water Resource Department (OWRD), and water managers are looking for methods to better regulate the aquifer when wells run dry. However, the faults are a big unknown when considering how to enforce the prior appropriation doctrine where junior permit holders are regulated off in times of water shortage. If a junior and senior water permit holder have wells that are separated by a fault, is it likely that stopping the junior permittee’s water use would actually result in more water available to the senior permit holder?

My approach is not novel in my scientific field. Several studies have evaluated similar parameters elsewhere in the Columbia River Basalts and also used statistical methods, but have not focused specifically on faults.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

Arc-GIS: Highly proficient in Desktop and Pro

R – novice, can copy/paste/edit code to suit my basic needs

Python – novice, took a class two years ago but have forgotten much of it

Image processing – working knowledge of ENVI from GEOG 580, and of Gravit from GEOG 572

Other spatiotemporal analysis – I haven’t really worked with software besides ArcGIS or QGIS?