Articles | Volume 19, issue 6
Hydrol. Earth Syst. Sci., 19, 2925–2942, 2015
https://doi.org/10.5194/hess-19-2925-2015
Hydrol. Earth Syst. Sci., 19, 2925–2942, 2015
https://doi.org/10.5194/hess-19-2925-2015

Research article 24 Jun 2015

Research article | 24 Jun 2015

TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks

M. F. Müller and S. E. Thompson

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Cited articles

Anders, A. M., Roe, G. H., Hallet, B., Montgomery, D. R., Finnegan, N. J., and Putkonen, J.: Spatial patterns of precipitation and topography in the Himalaya, Geol. Soc. Am. Special Papers, 398, 39–53, 2006.
Bishop, G. D. and Church, M. R.: Automated approaches for regional runoff mapping in the northeastern United States, J. Hydrol., 138, 361–383, 1992.
Belisle, C. J. P.: Convergence theorems for a class of simulated annealing algorithms on Rd, J. Appl. Probab., 29, 885–895, 1992.
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H.: Runoff prediction in ungauged basins: Synthesis across processes, places and scales, Cambridge University Press, 2013.
Bosch, D., Sheridan, J., and Davis, F.: Rainfall characteristics and spatial correlation for the Georgia Coastal Plain, Trans. ASAE, 42, 1637–1644, 1999.
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Short summary
We introduce TopREML as a method to predict runoff signatures in ungauged basins using linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is accounted for by allowing for stronger correlations between flow-connected basins. The restricted maximum likelihood framework provides best linear unbiased predictions of both the predicted flow variable and its uncertainty as shown in Monte Carlo and cross-validation analyses in Nepal and Austria.