Attribution of streamflow changes across the globe based on the Budyko framework

Area of Science:

• Hydrology
• Environmental Science
• Climate Change Research

Background:

• Understanding streamflow change drivers is crucial for predicting hydrological responses to environmental shifts.
• Global assessments of streamflow change attribution are debated due to limited hydrological station data.
• Previous studies often rely on reconstructed streamflow data, potentially leading to biased conclusions.

Purpose of the Study:

• To attribute observed global streamflow changes using a comprehensive dataset from 1960-2014.
• To compare attribution results from observed versus reconstructed streamflow data.
• To clarify the dominant factors influencing global streamflow variability.

Main Methods:

• Utilized the most comprehensive dataset available for global streamflow attribution.
• Analyzed streamflow changes between 1960 and 2014.
• Compared attribution results derived from observed streamflow data against those from reconstructed datasets.

Main Results:

• Factors other than precipitation (P) and potential evaporation (E0) were found to be the dominant drivers of observed global streamflow changes (48.9-50.9% of stations).
• Reconstructed streamflow datasets indicated precipitation as the dominant factor (72.3-72.9% of stations), contrasting with results from observed data.
• Attribution using reconstructed data may overestimate precipitation's role and underestimate other factors like vegetation and human impacts.

Conclusions:

• Observed streamflow data is essential for accurate attribution of streamflow changes, avoiding biases inherent in reconstructed datasets.
• Non-precipitation/evaporation factors significantly influence streamflow, with notable regional variations.
• This study underscores the need for empirical data in hydrological research to prevent skewed scientific understanding.