Uncertainty Assessment in Climate Change Impact on Hydrology

Assessing how climate change will alter hydrological regimes is beset by multiple layers of uncertainty, arising from climate projections, downscaling methods, hydrological model structure and parameterisation. Climate models differ in their representation of atmospheric processes, while statistical or dynamical downscaling introduces further variability in precipitation and temperature inputs. Hydrological models, whether lumped or distributed, carry structural uncertainty in process representation and parameter equifinality, meaning that different parameter sets may yield comparable performance under historical conditions but diverge under novel climates. Ensemble approaches that combine multiple climate and hydrological models help to characterise the full range of plausible futures, revealing that precipitation uncertainty often dominates streamflow projections, especially for rapid response events such as floods, whereas model parameter uncertainty can be more influential for low‐flow and groundwater projections. Rigorous quantification of these uncertainties is vital for risk‐based water resources planning, adaptation strategy development and the prioritisation of observational and modelling efforts aimed at reducing key knowledge gaps. Concrete examples include varying projections of river discharge extremes and seasonal water availability across regions, highlighting the need for context‐specific adaptation measures and the careful interpretation of probabilistic outcomes for policy and infrastructure design.

Research from Nature Portfolio

Recent studies have dissected the relative contributions of climate and hydrological model uncertainties by comparing multi‐GCM ensembles with multi‐parameter ensembles within a unified framework. It was shown that uncertainty arising from the choice of global climate models exceeds hydrological parameter uncertainty for fast components such as direct runoff, whereas slow components like soil moisture and baseflow are more sensitive to model parameterisation. Further analysis demonstrated that precipitation projections contribute more to the overall spread in hydrological outcomes than temperature, underscoring the imperative to improve precipitation simulations in both global and regional climate models.

Uncertainty Assessment in Climate Change Impact on Hydrology publication trend

The graph below shows the total number of articles in uncertainty assessment in climate change impact on hydrology across all publications each year (not limited to Nature Index journals).

Technical terms

General Circulation Model (GCM): A numerical model that simulates the global climate system, including atmosphere, ocean and land surface processes, used to project future climate states.

Ensemble prediction: An approach that runs multiple models or multiple configurations of a model to capture the range of possible outcomes and quantify uncertainty.

Parameter equifinality: The phenomenon whereby different parameter sets yield similar model performance under calibration but diverge under new conditions.

Downscaling: Techniques used to translate coarse‐resolution climate model output to finer scales suitable for impact studies, either statistically or dynamically.

Shared Socioeconomic Pathway (SSP): A scenario framework that combines greenhouse gas emission trajectories with assumptions about socio‐economic development for climate change research.

References

1. Projections of flood regime changes over the upper-middle Huaihe River Basin in China based on CMIP6 models. Frontiers in Environmental Science (2023).
2. Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions. Scientific Reports (2019).
3. A Framework to Quantify the Uncertainty Contribution of GCMs Over Multiple Sources in Hydrological Impacts of Climate Change. Earth's Future (2020).
4. Hierarchy of climate and hydrological uncertainties in transient low-flow projections. Hydrology and Earth System Sciences (2016).