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Gauging the ungauged basin: relative value of soft and hard data


Seibert, Jan; McDonnell, J J (2015). Gauging the ungauged basin: relative value of soft and hard data. Journal of Hydrologic Engineering, 20(1):A4014004.

Abstract

The long-standing issue of hydrological predictions in ungauged basins has received increased attention due to the recent IAHS Decade on Predictions in Ungauged Basins (PUB) initiative. 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. In this study we explored the value of a rudimentary gauging campaign for making predictions in an ungauged basin. We used the well-studied Maimai watershed in New Zealand as a hypothetical ungauged basin where we pretended to start with no runoff data and added iteratively different sub-sets 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, we explored different types of soft data to constrain the model calibration. Model simulations were validated using the available runoff data from different years. We found that surprisingly little runoff data was 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. Our findings based on 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.

The long-standing issue of hydrological predictions in ungauged basins has received increased attention due to the recent IAHS Decade on Predictions in Ungauged Basins (PUB) initiative. 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. In this study we explored the value of a rudimentary gauging campaign for making predictions in an ungauged basin. We used the well-studied Maimai watershed in New Zealand as a hypothetical ungauged basin where we pretended to start with no runoff data and added iteratively different sub-sets 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, we explored different types of soft data to constrain the model calibration. Model simulations were validated using the available runoff data from different years. We found that surprisingly little runoff data was 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. Our findings based on 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.

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8 citations in Web of Science®
5 citations in Scopus®
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Additional indexing

Item Type:Journal Article, not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2015
Deposited On:30 Jan 2014 15:48
Last Modified:05 Apr 2016 17:27
Publisher:American Society of Civil Engineers
ISSN:1084-0699
Additional Information:SPECIAL ISSUE: Grand Challenges in Hydrology
Publisher DOI:https://doi.org/10.1061/(ASCE)HE.1943-5584.0000861
Permanent URL: https://doi.org/10.5167/uzh-89515

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