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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.

Simon Figura^1,2, David M Livingstone², Rolf Kipfer^1,2,3

¹ETH Zurich, Institute of Biogeochemistry and Pollutant Dynamics, Universitätsstrasse 16, CH-8092, Zürich, Switzerland.

|November 22, 2014

Summary

This summary is machine-generated.

Groundwater temperatures in Switzerland are projected to rise by 1.1 to 3.8 K by 2100 due to climate warming. This forecast uses a regression model linking groundwater and air temperatures under various emissions scenarios.

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Area of Science:

Environmental Science
Hydrology
Climate Science

Background:

Groundwater temperature influences biogeochemical processes.
Few studies forecast groundwater temperature responses to climate change.
Understanding these changes is crucial for water resource management.

Purpose of the Study:

To forecast groundwater temperature changes in Swiss aquifers by the end of the century.
To assess the impact of future climate warming on groundwater systems.
To evaluate a modeling approach for predicting groundwater temperature dynamics.

Main Methods:

Utilized a composite linear regression model.
Analyzed the lagged relationship between historical groundwater and regional air temperatures.
Employed regional air temperature projections from greenhouse-gas emissions scenarios (A2, A1B, RCP3PD).

Main Results:

The modeling approach is adequate for long, variable datasets, particularly for riverbank-infiltrated aquifers.
Groundwater temperatures in three Swiss aquifers are predicted to increase.
Projected increases range from 1.1 to 3.8 K compared to the 1980-2009 baseline, varying by emissions scenario.

Conclusions:

Groundwater temperature in studied Swiss aquifers will likely increase due to climate warming.
The magnitude of the temperature increase depends on the chosen greenhouse-gas emissions scenario.
Effective forecasting requires sufficient historical data length and variability for model calibration.