Walnut River Valley Archaeology
A Multi-faceted Approach to Survey
"This bluff overlooks the spot where many believe Spanish conquistador Juan de Oñate met a delegation of Etzanoans"- David Kelly of the Los Angeles Times
A Lost City in Kansas?
Previous archaeological work in the Walnut Valley basin includes another Wichita State University anthropologist and archaeology professor, Dr. Donald Blakeslee, where he began piecing together evidence of an ancient civilization, in what appeared to be rural field in Arkansas City, Kansas. The lost city of Etzanoa is thought to have existed between 1450 and 1700.
Along the banks of the Walnut and Arkansas River, about 20,000 Etzanoans lived in beehive-shaped houses during the time period. Left behind, artifacts of pottery and arrowheads have been uncovered and used to aid in exploring and understanding this archaeological site.
The Walnut River Valley Study
Dr. David Hughes, Associate Professor in the Department of Anthropology at Wichita State, has begun a 10-year program of survey, assessment, and evaluation of the archaeology of the Walnut River Valley in the Flint Hills of Kansas.
Due to its terrace and alluvial features, the Walnut Valley holds significant and accessible archaeological records that represents the last 10,000 years of Flint Hills Prehistory.
Purpose and Motivation
Our record of the archaeological past is vanishing at an alarming rate. The Walnut River Valley is beginning to develop more intensively and more sites are being lost. There are new problems that are developing that could have harmful consequences for the future of the Walnut Valley
- No-till farming is not exposing buried materials as it did, so the surface record of known archaeological sites is becoming sparse
- The collectors and landowners who have collections or know of materials on their property are beginning to lose interest or time- and the knowledge they should be recorded
- Developing a long-term plan of recording, analysis, and action to preserve this unique part of our heritage will assist local communities in planning for exigencies of the 21st century.
Creating a Model
Before being able to create a model using ArcGIS, preliminary data must be collected. Data sets used for this study include:
- Political boundaries
- Modern cultural features
- Hydrology
- Soils
- Geology
- Botany and ground cover
- Known archaeological sites
- Contemporary, historical, and aerial maps dating to the 1870's
Environmental Data
Bedrock geology map compared to surface alluvial map, both indicating archaeological sites
Site Data
A composite of known archaeologic sites in the Walnut River study area. As part of the investigations, more accurate information about age, cultural affiliation, and technology will be developed on these sites.
Site distribution on the landscape is important to note
Conclusion: there is a correlation of site location and access to water
Historical and Archival Maps
The use of historic data will allow for a better understanding of the past 1875 settlement system and how it has changed in response to industrial technology.
These maps can be used to track stream channel migrations through time in the research area
The Future Steps
After individually examining the data sets, the nucleus of the model has been created using ArcGIS
Preliminary review of these data provide strong evidence that differential archaeological site locations are connected to the specific and recognizable elements of the local environment such as remnant terrace systems, specific geologic resources such as lithies, and the local hydrology.
The future of the Walnut River Valley includes refining the data requirements to suit changing research needs as well as search for more accurate data to fit GIS model including:
- More accurate elevation and stream data
- Comprehensive soils maps
- Improved outcrop geology
- Expand on hydrography, insolation, and other studies
In addition to the mentioned above, refining ages of the known sites by reexamining its collections as well as sending more people out into the field with minimal information to improve content
