The Budyko hypothesis provides a first-order estimate of water partitioning into runoff (Q) and evapotranspiration (E). Observations, however, often show significant departures from the Budyko curve; moreover, past improvements to Budyko curve tend to lose predictive power when migrated between regions or to small scales. Here to estimate departures from the Budyko curve, we use hydrologic signatures extracted from Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage anomalies. The signatures include GRACE amplitude as a fraction of precipitation (A/P), interannual variability, and 1-month lag autocorrelation. We created a group of linear models embodying two alternate hypotheses that departures can be predicted by (a) Taylor series expansion based on the deviation of physical characteristics (seasonality, snow fraction, and vegetation index) from reference conditions and (b) surrogate indicators covarying with E, e.g., A/P. These models are fitted using a mesoscale USA data set (HUC4) and then evaluated using world data sets and USA basins <1 × 105 km2. The model with A/P could reduce error by 50% compared to Budyko itself. We found that seasonality and fraction of precipitation as snow account for a major portion of the predictive power of A/P, while the remainder is attributed to unexplained basin characteristics. When migrated to a global data set, type b models performed better than type a. This contrast in transferability is argued to be due to data set limitations and catchment coevolution. The GRACE-based correction performs well for USA basins >1000 km2 and, according to comparison with other global data sets, is suitable for data fusion purposes, with GRACE error as estimates of uncertainty.
All Science Journal Classification (ASJC) codes
- Water Science and Technology