The Statistical Adjustment Tool: Methodological Transformations of Hydrologic Models for Improved Flow Behavior Modeling

General circulation models (GCMs), while effectively used to forecast weather conditions and climate patterns, often lack the required granularity for precise analysis at a particular location. This limitation is revealed by inconsistencies between observed climate data and GCM hindcasts, where climate scientists frequently apply statistical transforms to better fit model outputs to observed data. These adjustments make GCM projections more precise; by applying the same transform to model forecasts, adjusted outputs better align with expected trends at a given location. Hydrologists face similar challenges with flow data: when adjusted climate data is used to power hydrologic models, hindcasts often fail to adequately match observed flow data, suggesting limited forecasting precision.

In novel application of this defined adjustment methodology, USACE has developed the Statistical Adjustment Tool, which transforms hydrologic model hindcasts to match observed flow data at a given location. These statistical transforms are then applied to projected flows, providing decision makers with more precise measures of forecasted hydrologic behavior. In use, the tool emphasizes transparency, where users assess the performance of various transformation techniques (e.g., quantile mapping or parametric transformation) both visually and with statistical measures of error and bias (e.g., root mean square error or percent bias). More generally, the tool helps the user better understand innate inconsistencies between a given hydrologic model and the observed data, as well as the measured improvements of the applied statistical adjustments. For a selected location, the user can compare various models and transformations to identify the optimal approach, leveraging the adjusted model to forecast hydrologic behavior. In our work, the impact of our methodology is demonstrated through a series of vignettes.

While the tool is valuable to planners, engineers, and scientists alike, its transparency also helps uncover deficiencies in existing climate models. By combining analytic outputs and methodological learnings, researchers can better identify the drivers of variation in GCM-based projections, prompting advancements in climate models, statistical adjustments, and climate forecasting methodologies.