Data Summary

Highest Groundwater Recharge Potential Areas, Susquehanna River Basin

2023 - Susquehanna River Basin Commission SRBC

API
REST: https://mapservices.pasda.psu.edu/server/rest/services/pasda/SRBC/MapServer
WMS: https://mapservices.pasda.psu.edu/server/services/pasda/SRBC/MapServer/WMSServer?request=GetCapabilities&service=WMS

ADDITIONAL RESOURCES
n/a

ABSTRACT
Different geologic materials, structures, and land uses all influence the rate in which water can recharge underlying aquifers. This dataset combines factors influencing recharge through standardization and weighting assignments using the Multi-Criteria Decision Analysis (MCDA)-GIS framework to identify land-surface areas that have the potential to provide a large fraction of recharge. Percent impervious cover, land surface slope, percent sand and clay, depth to bedrock, drainage density, karst density, and fault density are used to describe recharge potential within the Susquehanna River Basin (basin). Many of these data layers are used to predict baseflow via regional regression equations in ungaged locations throughout the Mid-Atlantic region; baseflow is often used as an approximation of recharge. Input criteria were prioritized using the Analytic Hierarchy Process (AHP), which is an additive weighting model that can be combined with MCDA. Three general “first-level factors” were identified based on three primary zones of infiltration or recharge; those include the land surface, shallow-subsurface (soil) geology, and structural/bedrock geology. Weighting assignments of input datasets are presented in the table below.WeightFirst-Level FactorsWeightSecond-Level Factors40Land Cover / Terrain25Percent Impervious15Land Surface Slope20Shallow-Subsurface Geology15Percent Sand2.5Percent Clay2.5Depth to Bedrock40Structural / Bedrock Geology25Drainage Density10Karst Density5Fault DensityRecharge potential is described as an index from 100-500, with 100 illustrating areas of least recharge potential and 500 illustrating areas of highest recharge potential. This dataset can be extracted and reclassified in user defined areas for local assessments using the quantile classification scheme.Reclassification of input datasets, and dataset sources are described in the "Lineage" section of the metadata.