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Background For effective wellhead protection, the area where water carrying potential contaminants can enter the groundwater system and flow to the supply well must first be defined, and then best management practices need to be implemented to minimize the opportunity for contamination to occur in areas defined as sources of water to the well. Determination of the sources of water and contributing areas to wells is complex because aquifers and their connection with recharge sources are heterogeneous in nature and hidden from direct observation. The major groundwater source for public supplies in upstate New York are valley-fill aquifers of glacial and post-glacial origin. Saturated coarse-grained sediments (sand...
Municipal Separate Storm Sewer System (MS4) permitees including the California Department of Transportation need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff. These entities also need information about the potential effectiveness of stormwater best management practices (BMPs) used to mitigate the effects of runoff. This information is needed to address total maximum daily load (TMDL) regulations. This model archive describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for assessing long-term annual yields of highway and urban runoff in selected areas of California with version 1.1.0 of the Stochastic Empirical Loading...


    map background search result map search result map Simulation of Contributing Areas to Selected Public Water-Supply Wellfields in the Valley-Fill Aquifers of New York State Model archive for analysis of long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM) Simulation of Contributing Areas to Selected Public Water-Supply Wellfields in the Valley-Fill Aquifers of New York State Model archive for analysis of long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM)