Abstract

Accurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. the commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate-sensitive regions (e.g., semi-arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilizing a semi-arid region in central India as a testbed. the implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. the results highlight the non-stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty-constraining approaches. Moreover, the impact of non-stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. in the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid-century onwards, the bias-adjustment approaches, and climate change scenarios significantly shape runoff projection uncertainties under non-stationary conditions. These findings emphasize the potential of climate bias and runoff sensitivity-Based GCM selection for reducing runoff uncertainty in near-future assessment (2030s). for mid-term and long-term runoff projections, addressing diverse climate biases through bias-adjustment approaches is more viable. This study offers critical insights to prioritize the development of a non-stationarity-Based approach for reliable runoff projections in climate-sensitive regions.

Department(s)

Biological Sciences

Publication Status

Open Access

Comments

University of Texas at Austin, Grant 80NSSC21K1008

Keywords and Phrases

climate biases; CMIP6 models; constrained runoff uncertainty; non-stationarity; runoff sensitivity

International Standard Serial Number (ISSN)

1097-0088; 0899-8418

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Wiley; Royal Meteorological Society, All rights reserved.

Publication Date

30 Dec 2024

Included in

Biology Commons

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