Articles | Volume 27, issue 11
https://doi.org/10.5194/hess-27-2149-2023
https://doi.org/10.5194/hess-27-2149-2023
Research article
 | 
07 Jun 2023
Research article |  | 07 Jun 2023

Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments

Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, and Hubert H. G. Savenije

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Manuscript not accepted for further review
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Cited articles

Bao, A. M., Liu, H. L., Chen, X., and Pan, X. l.: The effect of estimating areal rainfall using self-similarity topography method on the simulation accuracy of runoff prediction, Hydrol. Process., 25, 3506–3512, https://doi.org/10.1002/hyp.8078, 2011. 
Bouaziz, L. J. E., Steele-Dunne, S. C., Schellekens, J., Weerts, A. H., Stam, J., Sprokkereef, E., Winsemius, H. H. C., Savenije, H. H. G., and Hrachowitz, M.: Improved understanding of the linkbetween catchment-scale vegetation accessible storage and satellite-derivedSoil Water Index, Water Resour. Res., 56, e2019WR026365, https://doi.org/10.1029/2019WR026365, 2020. 
Boyd, M. J., Bates, B. C., Pilgrim, D. H., and Cordery, I.: WBNM: A General Runoff Routing Model Computer Programs and User Guide, Water Research Laboratory, The University of New South Wales, https://doi.org/10.4225/53/57996b382f17b, 1987. 
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernandez, J., and Llorens, P.: Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe, Remote Sens. Environ., 115, 3390–3408, https://doi.org/10.1016/j.rse.2011.08.003, 2011. 
Carroll, D.: URBS a Rainfall Runoff Routing Model for flood forecasting and design version 4.00, https://www.scribd.com/document/93746264/URBSManualV440 (last access: 15 January 2020), 2004. 
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Short summary
We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.