Gauging the Ungauged Basin: Relative Value of Soft and Hard Data

Abstract

The long-standing issue of hydrological predictions in ungauged basins has received increased attention due to the recent International Association of Hydrological Sciences (IAHS) decade on predictions in ungauged basins (PUB). Since the outset of PUB, many have noted that the best way to confront an ungauged basin is to first make some basic streamflow measurements. This study explores the value of a rudimentary gauging campaign for making predictions in an ungauged basin. The well-studied Maimai watershed in New Zealand was used as a hypothetical ungauged basin, and this study was designed to start with no runoff data and add iteratively different subsets of the available data to constrain the calibration of a simple three-reservoir conceptual catchment model. These subsets included single runoff events or a limited number of point values—in other words, what could be measured with limited, campaign-like field efforts in an ungauged basin. In addition, different types of soft data were explored to constrain the model calibration. Model simulations were validated using the available runoff data from different years. It was found that surprisingly little runoff data were necessary to derive model parameterizations that provided good results for the validation periods, especially when these runoff data were combined with soft data. The relative value of soft data increased with decreasing amount of streamflow data. The findings from the Maimai watershed suggest that, when starting with no flow information, one event or 10 observations during high flow provide almost as much information as three months of continuously measured streamflow for constraining the calibration of a simple catchment model.