An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact...

Area of Science:

• Environmental science
• Climate science
• Hydrology

Background:

• Hydro-climatic projections involve complex production chains leading to uncertainties.
• Large ensembles of projections are used to characterize uncertainty but are often too extensive for practical decision-making.
• Existing subsets of projections may not be informationally representative.

Purpose of the Study:

• To propose a framework for identifying a representative subset from large hydro-climatic projection ensembles.
• To apply the Maximum Information Minimum Redundancy (MIMR) concept for subset selection.
• To assess the representativeness of selected subsets based on climate variables and hydrological impacts.

Main Methods:

• Utilized the Maximum Information Minimum Redundancy (MIMR) information-theoretic framework.
• Analyzed an ensemble of 16 precipitation and temperature projections for Sweden.
• Employed the HBV hydrological model to project river discharge.
• Evaluated subset representativeness using statistical properties of precipitation, temperature, and discharge.

Main Results:

• A representative subset comprising 25-33% of the full ensemble can capture over 80% of the total information on hydro-climatic changes.
• The optimal subset composition is sensitive to regional characteristics, variable choice, seasons, and future periods.
• The selection process should incorporate impact model variables, not just climatic variables.

Conclusions:

• The MIMR-based framework effectively identifies informationally representative subsets of hydro-climatic projections.
• A significantly smaller subset of projections can be sufficient for decision-making while retaining essential information.
• Future selection strategies must consider both climate and impact variables for robust hydro-climatic assessments.