Comparative Evaluation of Five Hydrological Models in a Large-Scale and Tropical River Basin

Abstract

Hydrological modeling is an important tool for water resources management, providing a feasible solution to represent the main hydrological processes and predict future streamflow regimes. The literature presents a set of hydrological models commonly used to represent the rainfall-runoff process in watersheds with different meteorological and geomorphological characteristics. The response of such models could differ significantly for a single precipitation event, given the uncertainties associated with the input data, parameters, and model structure. In this way, a correct hydrological representation of a watershed should include the evaluation of different hydrological models. This study explores the use and performance of five hydrological models to represent daily streamflow regimes at six hydropower plants located in the Tocantins river basin (Brazil). The adopted models include the GR4J, HYMOD, HBV, SMAP, and MGB-IPH. The evaluation of each model was elaborated considering the calibration (2014–2019) and validation period (2005–2010) using observed data of precipitation and climatological variables. Deterministic metrics and statistical tests were used to measure the performance of each model. For the calibration stage, results show that all models achieved a satisfactory performance with NSE values greater than 0.6. For the validation stage, only the MGB-IPH model present a good performance with NSE values greater than 0.7. A bias correction procedure were applied to correct the simulated data of conceptual models. However, the statistical tests exposed that only the MGB-IPH model could preserve the main statistical properties of the observed data. Thus, this study discusses and presents some limitations of the lumped model to represent daily streamflows in large-scale river basins (>50,000 km2).

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

“…Notably, the model consistently underestimates the streamflow for the Sunkoshi river basin, Figure 5b. This could be attributed to the underestimation of the precipitation (snowmelt and rainfall) by the meteorological observatory stations as reported by Asian Development Bank (2019), Devkota and Gyawali (2015), Pokhrel et al. (2014), and Savéan et al.…”

“…Moreover, the high spatial variability in the precipitation volumes with the elevation due to orographic effects further worsens the situation leading to significant underestimation. The solid precipitation for which the classical rain gauges are usually not suitable, further worsens the estimation (Asian Development Bank, 2019; Savéan et al., 2015). Another possible reason could be the erroneous data in the observed flow (Devkota & Gyawali, 2015; Pokhrel et al., 2014).…”

Impact of Hydroclimatic Changes on Water Security in the Cantareira Water Production System, Brazil

“…Regarding the reliability of the data produced by this study, inherent uncertainties are associated with climate models [51], which may be further amplified in hydrological modeling [52]. Despite these challenges, we have ensured that our data sources are reliable, and the hydrological model has undergone meticulous calibration.…”

Comparison of the application of HBV and HEC-HMS hydrology models for accessing climate change in the upper Citarum Watershed, Indonesia

“…Determination of surface runoff, interflow and base flow in the HBV model uses the concept of a flow tank located at the outlet of the watershed. The HBV model flow system generally has two tanks, namely the first tank model is close to surface runoff and the second tank is used to simulate baseflow which is the contribution of groundwater [22,23].…”