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

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

Sriwongsitanon, Nutchanart (Kasetsart University)
Jandang, Wasana (Kasetsart University)
Williams, James (GRC Hydro Pty Ltd)
Suwawong, Thienchart (Kasetsart University)
Maekan, Ekkarin (Kasetsart University)
Savenije, Hubert (TU Delft Water Resources)

2023

A parsimonious semi-distributed rainfall–runoff model has been developed for flow prediction. In distribution, attention is paid to both the timing of the runoff and the 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 five gauged sub-catchments at which internal performance is evaluated. To test the effect of timing, first the 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 the 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 River basin (UPRB), controlled by station P.1 located at the centre of Chiang Mai province. The outcome of FLEX-SD was compared to the (1) observations at the internal stations, (2) calibrated FLEXL model, and (3) the semi-distributed URBS model – another established semi-distributed rainfall–runoff model. FLEX-SD showed better or similar performance during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalised 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 two alternative models, namely FLEX-SD-NDIIMaxMin and FLEX-SD-NDIIAvg. The additional constraint on the moisture-holding capacity (Sumax) by NDII, particularly in FLEX-SD-NDIIAvg, improved both the model performance and the realism of its distribution across the UPRB, which corresponds linearly to the percentage of evergreen forests (R2=0.69). To check how well the models represents simulated root zone soil moisture (Sui), the performance of the FLEX-SD-NDII models was compared to the time series of the soil water index (SWI). The correlation between the Sui and the daily SWI appeared to be very good and was even better than the correlation with the NDII, which does not provide good estimates during wet periods. The SWI, which is model-based, was not used for calibration but appeared to be an appropriate index for validation.

http://resolver.tudelft.nl/uuid:e6ab0d20-4e76-47c2-94c0-4a7e2a714901

https://doi.org/10.5194/hess-27-2149-2023

1027-5606

Hydrology and Earth System Sciences, 27 (11), 2149–2171

Institutional Repository

journal article

© 2023 Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, Hubert Savenije