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Watershed Vulnerability Predictions for the Ozarks Using Landscape Models
Author(s) -
Lopez Ricardo D.,
Nash Maliha S.,
Heggem Daniel T.,
Ebert Donald W.
Publication year - 2008
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2007.0360
Subject(s) - watershed , streams , environmental science , hydrology (agriculture) , water quality , partial least squares regression , surface water , ecology , statistics , mathematics , environmental engineering , biology , computer network , geotechnical engineering , machine learning , computer science , engineering
Forty‐six broad‐scale landscape metrics derived from commonly used landscape metrics were used to develop potential indicators of total phosphorus (TP) concentration, total ammonia (TA) concentration, and Escherichia coli bacteria count among 244 sub‐watersheds of the Upper White River (Ozark Mountains, USA). Indicator models were developed by correlating field‐based water quality measurements and contemporaneous remote‐sensing–based ecological metrics using partial least squares (PLS) analyses. The TP PLS model resulted in one significant factor explaining 91% of the variability in surface water TP concentrations. Among the 18 contributing landscape model variables for the TP PLS model, the proportions of a sub‐watershed that are barren and in human use were key indicators of water chemistry in the associated sub‐watersheds. The increased presence and reduced fragmentation of forested areas are negatively correlated with TP concentrations in associated sub‐watersheds, particularly within close proximity to rivers and streams. The TA PLS model resulted in one significant factor explaining 93% of the variability in surface water TA concentrations. The eight contributing landscape model variables for the TA PLS model were among the same forest and urban metrics for the TP model, with a similar spatial gradient trend in relationship to distance from streams and rivers within a sub‐watershed. The E. coli PLS model resulted in two significant factors explaining 99.7% of the variability in E. coli cell count. The 17 contributing landscape model variables for the E. coli PLS model were similar to the TP and TA models. The integration of model results demonstrates that forest, riparian, and urban attributes of sub‐watersheds affect all three models. The results provide watershed managers in the Ozark Mountains with a broad‐scale vulnerability prediction tool, focusing on TP, TA, and E. coli , and are being used to prioritize and evaluate monitoring and restoration efforts in the vicinity of the White River, a major tributary to the Mississippi River and Gulf of Mexico.