Comparative assessment of predictions in ungauged basins – Part 1: Runoff-hydrograph studies

Abstract

Abstract. The objective of this assessment is to compare studies predicting runoff hydrographs in ungauged catchments. The aim is to learn from the differences and similarities between catchments in different locations, and to interpret the differences in performance in terms of the underlying climate and landscape controls. The assessment is performed at two levels. The Level 1 assessment is a metaanalysis of 34 studies reported in the literature involving 3874 catchments. The Level 2 assessment consists of a more focused and detailed analysis of individual basins from selected studies from Level 1 in terms of how the leave-one-out cross-validation performance depends on climate and catchment characteristics as well as on the chosen regionalisation method. The results indicate that runoff-hydrograph predictions in ungauged catchments tend to be more accurate in humid than in arid catchments and more accurate in large than in small catchments. The dependence of performance on elevation differs by regions and depends on how aridity varies with elevation and air temperature. The effect of the parameter regionalisation method on model performance differs between studies. However, there is a tendency towards a somewhat lower performance of regressions than other methods in those studies that apply different methods in the same region. In humid catchments spatial proximity and similarity methods perform best while in arid catchments similarity and parameter regression methods perform slightly better. For studies with a large number of catchments (dense stream gauge network) there is a tendency for spatial proximity and geostatistics to perform better than regression or regionalisation based on simple averaging of model parameters from gauged catchments. There was no clear relationship between predictive performance and the number of regionalised model parameters. The implications of the findings are discussed in the context of model building.

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

“…The findings of the present study do not support the results of previous studies that reported better performance of the nearest neighbor than the physical similarity (e.g. Merz and Blöschl, 2004;Parajka et al, 2005;Oudin et al, 2008;Zhang and Chiew, 2009;Parajka et al, 2013).…”

“…Parajka et al (2013) categorized parameter regionalization methods into five groups. The first method is based on regional calibration by utilizing data from multi-sites in the region.…”

“…Hence, dependency of the regionalization on the performance measures is also an additional challenge in the regionalization endeavors, which needs consideration. Further references on regionalization works can be found from review papers by He et al (2011), Razavi and Coulibaly (2013) and Hrachowitz et al (2013), and the synthesis by Parajka et al (2013).…”

“…No universally best-performing regionalization method, model structure or evaluation criteria could be suggested due to the peculiarities of catchments in different climate regimes and landscapes (see a recent synthesis by Parajka et al, 2013).…”

Predicting streamflows in snowmelt-driven watersheds using the flow duration curve method

“…In the case of conceptual modeling, regionalization of parameters for ungauged watersheds were categorized by three approaches (Peel and Blöschl, 2011): (a) regression analysis between individual parameters and watershed properties (e.g., Kim and Kaluarachchi, 2008;Gibbs et al, 2012); (b) parameter transfer based on spatial proximity (e.g., Vandewiele et al, 1991;Oudin et al, 2008); and (c) physical similarity (e.g., McIntyre et al, 2005;Oudin et al, 2008Oudin et al, , 2010. Even if the performance of these three approaches was dependent on climatic conditions, performance and complexity of the model, and other factors, several studies concluded that the spatial proximity method was attractive due to its better performance and simplicity (Oudin et al, 2008;Parajka et al, 2013). Hence, this study used the proximity-based regionalization for regulated watersheds.…”