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Discussion papers
https://doi.org/10.5194/hess-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 10 Mar 2020

Submitted as: research article | 10 Mar 2020

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This preprint is currently under review for the journal HESS.

Using NDII pattern for a semi-distributed rainfall-runoff model 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 10 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, before adding to runoff of 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 P.1; 2) the results of the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better performance than URBS, but a bit lower than the calibrated FLEXL model with NSE of 0.74, 0.71, and 0.76, respectively. Subsequently, at the level of the gauged internal sub-catchments, runoff estimates of FLEX-SD were compared to observations and calibrated FLEXL model results. The results demonstrate that FLEX-SD provides more accurate runoff estimates at P.1, P.67 and P.75 stations which are located along the main Ping River, compared to those provided by the lumped calibrated FLEXL model. The results were less good at 2 tributary stations (P.20 and P.21), where calibrated FLEXL output performed better, while performance was similar at one tributary station (P.4A). Overall, FLEX-SD performed better than URBS at 5 out of 6 stations except at P.21.

Subsequently, the effect of distributing moisture storage capacity was tested. Since the FLEX-SD uses the same Sumax value - the maximum moisture holding capacity of the root zone - for all sub-catchments, FLEX-SD-NDII was set-up making use of the spatial distribution of the NDII (the normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone, particularly during dry periods. The maximum moisture holding capacity in the root zone assumed to be a function of the maximum seasonal range of NDII values. The spatial distribution of this range among sub-catchments was used to calibrate the semi-distributed FLEX-SD-NDII model. The additional constraint by the NDII improved the performance of the model and the realism of the distribution. To test 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 verification.

Nutchanart Sriwongsitanon et al.

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Short summary
In this paper we present a method to distribute crucial model parameters over subcatchments so as to enhance overall rainfall-runoff performance. We also analyse how soil moisture indicators can be used to distribute root zone moisture capacity over subcatchments. NDII proves to be very effective for this purpose, resulting in better overall model performance, good temporal correspondence between modelled soil moisture and the SWI, and improved recession behavior and dry season flow.
In this paper we present a method to distribute crucial model parameters over subcatchments so...
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