Streamflow prediction under extreme data scarcity: a step toward hydrologic process understanding within severely data-limited regions

Abstract

ABSTRACTStreamflow prediction in ungauged basins is necessary to support water resources management decisions. Herein we refine and evaluate the Streamflow Prediction under Extreme Data-scarcity (SPED) model, a framework designed for streamflow prediction within regions of sparse hydrometeorological observation. With the SPED framework, inclusion of soft data directs optimization to balance runoff efficiency with the selection of hydrologically representative parameters. Here SPED is tested in catchments around the world, including four well-gauged catchments, by mimicking data-scarcity and comparing against data-intensive approaches. By differentiating equifinal models, SPED succeeds where traditional approaches are likely to fail: partially dissimilar reference/target catchments. For instance, in a pair of reference/target catchments with different base flow regimes, SPED outperforms a model calibrated only to maximize efficiency (NSE of 0.54 versus 0.08). SPED performs consistently (NSE range: 0.54–0.74) across the diverse climatological and physiographic settings tested and proves comparable to state-of-the-science methods that use robust data networks.