Preprints
https://doi.org/10.5194/hess-2021-598
https://doi.org/10.5194/hess-2021-598
 
20 Dec 2021
20 Dec 2021
Status: a revised version of this preprint is currently under review for the journal HESS.

Using NDII patterns to constrain semi-distributed rainfall-runoff models in tropical nested catchments

Nutchanart Sriwongsitanon1,2, Wasana Jandang1,2, Thienchart Suwawong1,2, and Hubert H. G. Savenije3 Nutchanart Sriwongsitanon et al.
  • 1Department of Water Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
  • 2Remote Sensing Research Centre for Water Resources Management (SENSWAT), Faculty of Engineering, Kasetsart University, Bangkok, Thailand
  • 3Delft University of Technology, Stevinweg 1, 2600 GA Delft, The Netherlands

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at the internal stations; 2) the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better or similar performance both during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values, and the annual average NDII values to construct 2 alternative models: FLEX-SD-NDIIMax-Min and FLEX-SD-NDIIAvg, respectively. The additional constraint on the moisture holding capacity by the NDII improved both model performance and the realism of the distribution. Distribution of Sumax using annual average NDII values was found to be well correlated with the percentage of evergreen forest in 31 sub-catchments. Spatial average NDII values were proved to be highly corresponded with the root zone soil moisture of the river basin, not only in the dry season but also in the water limited ecosystem. To check how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for validation.

Nutchanart Sriwongsitanon et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-598', Anonymous Referee #1, 02 Feb 2022
    • CC1: 'Reply on RC1', Nutchanart Sriwongsitanon, 05 Feb 2022
    • CC2: 'Reply on RC1', Nutchanart Sriwongsitanon, 04 Mar 2022
    • CC3: 'Reply on RC1', Nutchanart Sriwongsitanon, 09 Mar 2022
    • CC4: 'Reply on RC1', Nutchanart Sriwongsitanon, 10 Mar 2022
    • AC1: 'Reply on RC1', Hubert H.G. Savenije, 12 Apr 2022
    • AC3: 'Reply on RC1', Nutchanart Sriwongsitanon, 20 Apr 2022
  • RC2: 'Comment on hess-2021-598', Anonymous Referee #2, 22 Mar 2022
    • CC5: 'Reply on RC2', Nutchanart Sriwongsitanon, 31 Mar 2022
    • AC2: 'Reply on RC2', Hubert H.G. Savenije, 12 Apr 2022
    • AC4: 'Reply on RC2', Nutchanart Sriwongsitanon, 20 Apr 2022

Nutchanart Sriwongsitanon et al.

Nutchanart Sriwongsitanon et al.

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Short summary
In this paper we developed semi-distributed rainfall-runoff models in predictive mode and provided better or similar performance compared to lumped models calibrated on internal stations. NDII proves to be effective to distribute root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. Soil moisture simulations are highly correlated with NDII in the dry season and water limited ecosystems, and agree with the SWI index in all seasons.