GEOMORPHOLOGY Pollution Sensitivity of Near-Surface Materials

How vulnerable is each watershed to groundwater pollution?

Why is this important for geomorphology?

The inherent vulnerability of groundwater to pollution is based upon an understanding of how the shape, type, and relative position of surface and subsurface geology (e.g., the geomorphic setting) influences water movement. The geomorphic setting controls the flowpaths of water and dissolved elements above and below the surface. Sandiness, permeability, depth to rock outcrops, fracturing and permeability of bedrock, flow restricting layers, and surface and subsurface connections all affect how vulnerable groundwater is to contamination from surface spills, leaking storage tanks, or other sources of waterborne pollution.

Pollution Sensitivity of Near-Surface Materials Health Scores

Geomorphology Index - Pollution Sensitivity of Near-Surface Materials

key for map

Creating the Index

Input Data
Pollution Sensitivity of Near-Surface Materials - state-wide view
Calculating the index

The index is based on the Pollution Sensitivity of Near-Surface Materials model which was developed and published by the MN DNR Geologic Atlas Staff as part of the Minnesota Hydrogeology Atlas Series HG-02 (Adams, 2016). The sensitivity to pollution was modeled based on the following inputs: surface soils from the USDA SSURGO database, and underlying geologic materials aggregated from several maps with a preference for the more recently created larger-scale maps.

The Near-Surface Pollution Sensitivity data is based on the travel time that it takes for water to infiltrate to a depth of 10 feet. The time to travel to a depth of 3 feet is calculated from soils data, and the time to travel from 3 feet to 10 feet is calculated from the surficial geology data. The total travel time is categorized into six sensitivity classes that range from high to ultra-low.

Some areas of the state have special geologic or land cover conditions that require individual consideration. One of these are areas associated with kart geology; karst areas consistently show very fast infiltration rates and a very high sensitivity to pollution. For the purposes of this analysis these areas are given a sensitivity rank of ‘very high’.

The remaining special conditions include: bedrock at or near the surface, disturbed surfaces (such as open-pit-mines), and peatlands. For watersheds where more than fifty percent of the land area is characterized by one or more of these special condition, the watershed is mapped with a unique symbol and the index score value is left empty. These areas may or may not be susceptible to groundwater pollution, but the level of sensitivity cannot be accurately made using the methods that are described here. 

This index score is delivered at both the major and catchment watershed scales, each scale is calculated independently using the same methods. To calculate the index score, an area weighted average is calculated for each watershed from the sensitivity rank. First, the categorical values are reclassified to numeric values.

 
Sensitivity Class Sensitivity Rank
Very High - Karst 0
High 1
Moderate 2
Low 3
Very Low 4
Ultra Low 5
Bedrock, Disturbed, Peatland Null

 

Next, the proportion of the watershed land area that has a numeric sensitivity rank is determined. If that area is greater than fifty percent, an area weighted average sensitivity is calculated for the watershed. If that area is less than fifty percent, no score is calculated and the value is left empty. Finally, the mean sensitivity ranks for all watersheds are rescaled from a range of 0 to 5 to a new range of 0 to 100. The following equation illustrates how the rescaled values are calculated.

 

Rescaled Value = (100 x Mean Sensitivity Rank) / 5

 

Index Results

Interpretation of results

The lowest index score values (high susceptibility) are in the karst region of the east and southeast along a diagonal toward the northwest. Most watersheds have moderate susceptibilitywith the highest scores (slight susceptibility) in the watersheds along the north and northeast.

The original model of ground water susceptibility (image above) shows a varying pattern of vulnerability across the state. Areas of highest ground water contamination susceptibility are in central, north-central, and east-central Minnesota in addition to the southeastern corner of the State in areas dominated by sand and gravel aquifers or in areas with karstic bedrock (Porcher, 1989). The highest susceptibility areas have high proportions of sand, gravel, sandstone, and/or karstic limestone, which are generally associated with moderate to high potential rates of recharge from surface or subsurface water sources. Since soil and subsurface deposits are generally course-textured and subsurface rocks do not form basin-sealing layers, water moves more easily both vertically downward from the surface and horizontally through the deposits. Furthermore, porous rock layers are generally found in these regions: either relatively porous or fractured sandstone, or karstic limestone with characteristic subsurface to surface channels.

The areas with the lowest ground water contamination susceptibility have opposite characteristics including: relatively impermeable surface deposits that reduce infiltration of surface water, low yielding aquifer materials with higher portions of clays and finer textured deposits at depth, and a very low to low recharge potential with reduced rates of water movement into or out of the deposits.

At a watershed scale, a high risk for groundwater contamination is prevalent in the karst landscape of the southeast. Other patterns of susceptibility follow general patterns in soil type and aquifer attributes. It is notable that some variability in levels of susceptibility across individual watersheds is lost due to averaging. This can be seen particularly along the Minnesota River and the beach ridges of the northwest. Also, the susceptibility to contamination is driven largely by subsurface features, often not readily apparent and divergent from the surface watershed boundaries.

Relationship to other health components

Water quality

This index is directly related to groundwater quality. The geomorphology of each watershed determines the risk level for groundwater contamination. This risk level should be used to inform decisions regarding appropriate land uses in vulnerable locations.

Connectivity

Groundwater and surface water are an interconnected resource. Water quality contamination at any point in the system degrades the ability of water to effectively provide ecosystem services throughout the connected hydrologic cycle.

Hydrology

This indicator is associated with the timing of the transfer between surface water and groundwater. In areas of high vulnerability, the exchange of surface water and ground water is more rapid, which impacts the hydrology of the system in a number of ways, for example, through a more flashy hydrograph.

Biology

Groundwater contamination can impact biological communities by directly affecting the health of plants and animals. Chemical and nutrient contaminants can enter lakes or streams through the groundwater and degrade the biological health of those systems.

Supporting Science

Scientific literature support

The index is based specifically on the Minnesota Hydrogeologic Atlas, HG-02: Pollution Sensitivity of Near-Surface Materials (Adams, 2016).

As described in the Hydrogeologic Atlas report: "The sensitivity to pollution of near-surface materials is an estimate of the time it takes for water to infiltrate the land surface to a depth of 10 feet. It is intended to estimate the time of travel through the unsaturated zone to reach the water table, which for the purposes of this method, is assumed to be 10 feet below land surface everywhere."

The connection between surface fertilizer and pesticide applications and groundwater contamination is documented in hundreds of studies over the past several decades, both nationally, and in the upper Midwest. For example, Nolan et al. (2002), found nitrate concentrations in ground water generally increases with higher nitrogen input and higher aquifer vulnerability, and consumers of shallow ground water are more likely to drink high-nitrate water. The median nitrate concentration and percent of wells from which water exceeds the EPA drinking-water standard for nitrate are highest in areas with high nitrogen input and high aquifer vulnerability. As another example, Barbash, (2001), found the commonly applied agricultural chemicals of atrazine, cyanazine, alachlor, and metolachlor were significantly correlated with the amount of agricultural land. Acetochlor, an agricultural herbicide first registered in 1994 for use in the USA, was detected in shallow ground water by 1995, consistent with previous field-scale studies indicating that some pesticides may be detected in ground water within 1 year following application.

Confidence in index

There is a well documented relationship between sensitivity of groundwater to contamination and the presence of contamination. Contaminants can be measured directly in the receiving waters and studies have shown higher levels of contamination where there is high susceptibility. This index evaluates the potential for additional degradation of groundwater rather than reporting existing impairments in groundwater quality.

The ranking process is a straightforward area weighted mean for each watershed based on the potential susceptibility to contamination from the model. The average for a watershed may mask smaller areas of very high or very low risk in the aggregated score.

Next Steps

Future enhancements

The location of and extent of groundwater aquifers relative to surface topography and watershed boundaries could be mapped and combined with information on the specific location and application rates for potential contaminants.

The current development of County Geologic Atlases across Minnesota provides more detailed information on groundwater resources and susceptibility. As County Geologic Atlases are completed, the improved data will be integrated into the model and updated index scores calculated.

Back to top