Use of the SWAT model for estimating reservoir volume in the Upper Navet watershed in Trinidad

Abstract

In tropical small islands the application of hydrological modelling is challenged by the scarcity of input data. Using in-situ and statistically estimated data, a hydrological model was calibrated and validated for the Upper Navet watershed in Trinidad, a small Caribbean island. The model was built using the soil water assessment tool (SWAT). The sensitivity analysis, calibration and validation were performed in SWAT calibration and uncertainty program (SWAT-CUP) using sequential uncertainty fitting (SUFI-2). The results revealed that for the estimated volume of water flowing into the reservoir (Flow_In) there were six sensitive parameters. To estimate the reservoir volume (Res_Vol), a modification of only the effective hydraulic conductivity was required. The model’s performance for the Flow_In validation showed acceptable values (R2 = 0.91 and NSE = 0.81). The uncertainty analysis indicated lower than recommended values for both the R-factor (0.46) and P-factor (0.31). For Res_Vol, the model’s validation performance indicated acceptable values (R2 = 0.72 and NSE = 0.70) and the P- and R-factors were 0.80 and 0.64, respectively. Based on the statistical metrics, the uncertainty for the Res_Vol was regarded as reasonable. However, care must be taken with the model’s use in the dry season, as the simulated Flow_In was generally over-predicted. A second validation of the model was performed for the reservoir under different negative (removal) and positive (addition) water amounts which confirmed the model’s ability to estimate the Res_Vol. The hydrological model established can therefore serve as a useful tool for water managers for the estimation of the Res_Vol at the Navet reservoir.

Modeling climate change impact on the inflow of the Magat reservoir using the Soil and Water Assessment Tool (SWAT) model for dam management

“…They pointed out that CN2, SOL_AWC, HRU_SLP, GW_DELAY, and CNCOEFF are the most sensitive parameters in the combined simulation analysis of streamflow and sediment yield. A reservoir modeling study by Beharry et al (2021) found 16 sensitive parameters (ALPHA_BNK, CH_K2, GW_DELAY, ALPHA_BF, CH_N2, CN2, EPCO, OV_N, REVAPMN, SURLAG, SOL_K, SOL_BD, GWQMN, SOL_AWC, and GW_REVA) in their study. Almost the same parameters were deemed sensitive in this study.…”

Estimations of future reservoir volumes under different climate scenarios for a tropical reservoir in a small Caribbean Island, Trinidad

“…In the calibration and validation of the model, the SUFI2 method was used and parameters within the hydrologic response unit, groundwater, streamflow, and soils were modified. Details and supporting evidence for the model performance statistics to estimate the volume of water in the Upper Navet Reservoir can be found in Beharry et al (2021). Figure 2 illustrates the flow chart for the calibration/validation of the model together with the main steps in the estimation of the future reservoir volume, under each RCP, for the short, medium, and long terms.…”

“…The inherited uncertainty of the calibrated and validated Navet hydrological model suggested that 71% of the observed data should be enveloped with the 95PPU (Beharry et al, 2021). It is acknowledged that additional sources of uncertainties may also have been added from the future climate conditions and concentration pathways, data assumptions, errors in the GCM predictors, errors in downscaling the precipitation, and errors in the SWAT model (Pervez & Henebry, 2015).…”

Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin

“…volumes (Beharry et al 2021), and explain rainfall-runoff relationships (Liu and Todini 2002) and assess hydrological impacts of global circulation patterns (Liang et al 1994), and determine occurrence of floods (Borah 2011;Costabile et al 2013;Costabile and Macchione 2015). The processing of hydrological parameters for the development of physically based models requires expertise, high-quality data, and detailed knowledge of the basin processes (Kim et al 2015a).…”