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PREDICTABILITY OF SURFACE WATER POLLUTION LOADING IN PENNSYLVANIA USING WATERSHED‐BASED LANDSCAPE MEASUREMENTS 1
Author(s) -
Johnson Glen D.,
Myers Wayne L.,
Patil Ganapati P.
Publication year - 2001
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2001.tb05515.x
Subject(s) - environmental science , watershed , land cover , hydrology (agriculture) , pollution , water quality , predictability , nonpoint source pollution , land use , spatial variability , agricultural land , statistics , mathematics , computer science , ecology , geology , geotechnical engineering , machine learning , biology
We formally evaluated the relationship between landscape characteristics and surface water quality in the state of Pennsylvania (USA) by regressing two different types of pollutant responses on landscape variables that were measured for whole watersheds. One response was the monthly exported mass of nitrogen estimated from field measurements, while the other response was a GIS‐modeled pollution potential index. Regression models were built by the stepwise selection protocol, choosing an optimal set of landscape predictors. After factoring out the effect of physiography, the dominant predictors were the proportion of “annual herbaceous” land and “total herbaceous” land for the nitrogen loading and pollution potential index, respectively. The strength of these single predictors is encouraging because the marginal land cover proportions are the simplest landscape measurements to obtain once a land cover map is in hand; however, the optimal set of predictors also included several measurements of spatial pattern. Thus, for watersheds at this general hierarchical scale, gross landscape pattern may be an important influence on instream pollution loading. Overall, there is strong evidence that using landscape measurements alone, obtained solely from remotely sensed data, can explain most of the water quality variability (R 2 = approx. 0.75) within these watersheds.