The University of Massachusetts Amherst
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Panel regression techniques for identifying impacts of anthropogenic landscape change on hydrologic response

TitlePanel regression techniques for identifying impacts of anthropogenic landscape change on hydrologic response
Publication TypeJournal Article
Year of Publication2013
AuthorsSteinschneider S, Yang Y-CEthan, Brown C
JournalWater Resources Research
Start Page7874
Date Published12/2013

 Statistical models relating anthropogenic modifications of the watershed landscape to alterations in streamflow are often plagued by heterogeneity in background watershed conditions and coinciding trends in climate that can complicate the interpretation of clear statistical relationships. This study introduces the use of panel regression as a modeling approach that can better accommodate basin heterogeneity and identify more robust signals between anthropogenic impacts on the watershed landscape and hydrologic response. Panel regression techniques pool multidimensional data recorded across individuals (i.e., watersheds) and through time to better characterize within- and between-variability in the hydrologic data set. The separate attribution of variability to both time and space dimensions enables the method to better identify response characteristics that are generalizable across watersheds. This study introduces in detail two broad classes of panel regression models (the fixed effects and random effects models) that may be particularly useful in the context of identifying and quantifying the impact of human-induced landscape alterations on hydrologic response. The technique is presented in a case study relating watershed urbanization to changes in the annual runoff coefficient for 19 watersheds in the Northeast United States. Comparisons are made against more standard cross-sectional and time-based regression analyses. The results show that the estimated relationship between urbanization and the annual runoff coefficient in this region is highly dependent on the dimensions of data examined (space or time) and model structure selected, with the most sophisticated and appropriate model suggesting that no significant relationship can be detected.