Panel regression techniques for identifying impacts of anthropogenic landscape change on hydrologic response

Abstract

[1] 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.

An empirical investigation of climate and land-use effects on water quantity and quality in two urbanising catchments in the southern United Kingdom

“…This study found that peak flows are between 30% to more than 100% greater in the urbanised area than in other streams. In contrast, Steinschneider et al (2013) reported little or no relationship between annual runoff coefficients and urbanisation in 19 catchments located in North East USA. In a study of observed flood events from catchments in the United Kingdom, Kjeldsen et al (2013) found that urbanisation tend to reduce lagtime and increase runoff volumes, resulting in higher peak flow values, with the lag-time reduction being the most important process.…”

Statistical Models to Predict and Assess Spatial and Temporal Low‐Flow Variability in New England Rivers and Streams

“…With long‐term persistence, serial autocorrelation decays much more slowly, and fractional autoregressive integrated moving average models must be applied to account for the long‐term memory in the system (Hosking ; Steinschneider et al. ). Lebo and Weber () caution that fractional differencing can only be applied reliably to time series with a length of at least 50 years, which is longer than the time series analyzed in the current study.…”

Panel regressions to estimate low‐flow response to rainfall variability in ungaged basins

“…To our knowledge, Steinschneider et al . [] were the first to suggest promising applications of PR to hydrologic applications by demonstrating its benefits in identifying robust generalized relations between anthropogenic landscape alterations and hydrologic response. In this study, MLR always refers to a model and TR and PR refer to the methods used to fit the MLR.…”

“…We thus expect that PR can be used to overcome both the statistical challenges of multicollinearity and omitted‐variable bias in MLRs. In addition, PR enables use of data across sites and through time simultaneously in a single regression, thus increasing the sample size for model error and coefficient estimation, and leading to improvements in the efficiency of the model‐coefficient estimates and model‐error variance [ Steinschneider et al ., ]. TR often imposes constraints on data such as requiring streamflow characteristics computed from concurrent records, which sometimes leads to extending records at short‐term sites or even disqualifying gaged sites that may be critical in data‐poor regions.…”