Component Level Scores
For the Hydrology Component, five health index scores are combined to create a mean (average) overall component health score.
What do the mean Hydrology index scores show?
Hydrology scores range from high in the northeast and some of the southeast blufflands, to very low in the southern and western part of the state. The primary drivers of these trends are low levels of perennial cover (year-round vegetation), and loss of water storage from wetland loss and drainage practices. Lower scores found in the Twin Cities metro region are due to high levels of impervious (hard cover) surface and high rates of water use.
Although it may mask extreme values in any one index, the mean serves to illustrate an overall gradient in results. The mean can also be used to compare watersheds, such as watersheds that are upstream or downstream within the same basin.
Hydrology Component Health Score
Creating the Index
- Input Data
Perennial Cover Data Layers:
National Land Cover Data (2001)
1890's Land Cover
Impervious Cover Data Layers:
Impervious Land Cover (2000 Satellite Data)
MN DNR Watersheds, All Catchments (2009)
Water Withdrawal Data Layers:
State Water Use Database (MN DNR, 2009)
County Well Inventory (2007)
National Wetland Inventory
Restorable Wetland Inventory (1992)
Ssurgo Hydric (saturated) soils (NRCS, 2009)
MN DNR Lakes and MN DNR Streams
Flow Variability Data Layers:
USGS Stream Flow Records (1980-2010)
Indicators of Hydrologic Alteration (IHA)
- Mean Hydrology Health Rankings
Across Minnesota the mean hydrology scores generally decrease from north to south and from east to west. The lowest mean score is found near the Twin Cities, reflecting the very low Water Withdrawal and Impervious Surface scores associated with high population density and high rates of development. Low scores are also found in other watersheds in southern and western Minnesota, particularly in the Minnesota River and Red River basins where loss of perennial cover and hydrologic storage is predominant.
Viewing the five input indices together reveals which index is contributing to these patterns.
Hydrology Index Inputs:
Click maps to explore each health index:
Perennial Cover Index
Impervious Cover Index
Water Withdrawal Index
Hydrologic Storage Index
Flow Variability Index
- Interpreting results
Mean hydrology scores are generally high in the northeastern third of the state, but the Flow Variability and Hydrologic Storage index values reveal some impacts that likely reflect mining activity and flow manipulation. Mean scores are also high in the extreme southeast bluffland area. The mean scores decrease quickly to the west as the steep valleys give way to urban and agricultural uses, which is established by the Perennial Cover and Hydrologic Storage indices.
Low mean scores are found in the southern and western portions of the state. These scores reflect low perennial cover and significant losses of hydrologic storage due to ditching, drainage and wetland removal.
Large amounts of impervious surface together with very high rates of water withdrawal resulted in low overall scores in the Twin Cities and St. Cloud watersheds. These index values also reflect the difficulty in comparing intense urban use of resources with the relatively low use in outstate Minnesota. Due to the large disparity in values being ranked on the same 0-100 scale, very high scores are assigned in outstate Minnesota. These scores mask important concerns about impervious surfaces and water use in these outstate watersheds. Additional data needs to be evaluated when reviewing these index values for non-metro watersheds.
The range of Flow Variability scores are relatively narrow across Minnesota. However, it should be noted that this Flow Variability index is a combination of several sub-index values that should be reviewed independently in order to determine which part(s) of the hydrologic cycle may be impacted in each watershed.
- Future enhancements
Relative scores that better reflect the variation in impervious surface and water withdrawal for outstate Minnesota could be developed. The flow variability index could utilize computations other than the mean of the sub-index values to create the score.