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USGS2010

New insights from well responses to fluctuations in barometric pressure

Hydrologists have long recognized that changes in barometric pressure can produce changes in water levels in wells. The barometric response function (BRF) has proven to be an effective means to characterize this relationship; we show here how it can also be utilized to glean valuable insights into semi-confined aquifer systems. The form of the BRF indicates the degree of aquifer confinement, while a comparison of BRFs between wells sheds light on hydrostratigraphic continuity. A new approach for estimating hydraulic properties of aquitards from BRFs has been developed and verified. The BRF is not an invariant characteristic of a well; in unconfined or semi-confined aquifers, it can change with conditions in the vadose zone. Field data from a long-term research site demonstrate the hydrostratigraphic insights that can be gained from monitoring water levels and barometric pressure. Such insights should be of value for a wide range of practical applications. ?? 2010 The Author(s). Journal compilation ?? 2010 National Ground Water Association.

Ground Water↗

USGS2010

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Journal of Environmental Management↗