Springs are unique ecosystems, but in many cases they are severely threatened and there is an urgent need for better spring management and conservation. To this end, we studied water quality and quantity in springs in Oulanka National Park, north-east Finland. Multivariate statistical methods were employed to relate spring water quality and quantity to hydrogeology and land use of the spring capture zone. This revealed that most springs studied were affected by locally atypical dolostone-limestone bedrock, resulting in high calcium, pH, and alkalinity values. Using Ward's hierarchical clustering, the springs were grouped into four clusters based on their water chemistry. One cluster consisted of springs affected by past small-scale agriculture, whereas other clusters were affected by the variable bedrock, e.g., springs only 1 km from the dolostone-limestone bedrock area were beyond its calcium-rich impact zone. According to a random forest model, the best predictors of spring water chemistry were spring altitude and the stable hydrogen isotope ratio of the water (delta H-2). Thus stable water isotopes could be widely applicable for boreal spring management. They may also provide a rough estimate of groundwater flow route (i.e., whether it is mainly local or regional), which largely determines the chemical characteristics of spring water. Our approach could be applied in other boreal regions and at larger spatial scales for improved classification of springs and for better targeted spring management. (C) 2015 Elsevier B.V. All rights reserved.
- Groundwater dependent ecosystems
- Water chemistry
- Stable isotopes of water
- Multivariate statistical methods
- Spring management
- Karstic Springs
- Random Forests