Assessing the Impact of Spatially Distributed Precipitation on Hydrological Modelling in Data-Scarce Mountainous Basin

Abstract

Hydrological modelling is an essential aspect of water resources management and has many applications, particularly water quality monitoring and the pollutant transmission. Recent research found that hydrology also plays a crucial role in plastic transport from land to the ocean, making it vital to understand the impact of spatially distributed precipitation on the hydrological processes. This leads to the question of how a hydrological model can help waste management and prevent plastic waste in the ocean. However, developing a suitable hydrological model with limited data availability, complex topography, and human interaction is a challenge. This research uses the Rio Las Vacas catchment as a case study and focuses on testing the applicability of the wflow hydrological model and assessing the impact of heterogeneous precipitation on the model result.

The research analysed the ground data available in Rio Las Vacas to verify and validate its quality. This study uses precipitation gauges, ERA5, and CHIRPS to assess the most suitable input for modelling rainfall-runoff in the Rio Las Vacas catchment. The catchment's hydrology was analysed and a conceptual model of the catchment was proposed. Using the wflow model, the hydrological response was modelled using the three rainfall products and the effect of uncertainty and spatially distributed rainfall was assessed.

The result shows that the Rio Las Vacas catchment suffers from a shortage of hydrometeorological information which is also of spurious quality. Only three rain gauges were available, and their analysis revealed high error variance and independence between stations. Hydrological characterisation analysis revealed significant human-induced changes. A calibrated wflow model effectively simulated the catchment’s hydrological response, demonstrating that global parameterisation was sufficient as a starting point for the model in this region. The soil water parameters are one of the most significant parameters in generating runoff for the model. The impact of spatially distributed precipitation is prominent in the model. This is one of the reasons for ERA5 to perform poorly in simulating the streamflow in the catchment. Based on the quantitative validation, the best modelling results came from using rain gauge data interpolated with the IDW method, followed by CHIRPS. However, the qualitative assessment shows CHIRPS is more reliable among the datasets.

In conclusion, this study shows the difficulties of modelling a data-scarce basin in the mountainous region. However, extensive data verification and validation, in combination with hydrological characterisation, helps to improve the model result. It also shows the precipitation inputs were crucial for a distributed model. The use of precipitation input with finer resolution could improve the model significantly. The study shows that the spatio-temporal distribution of the precipitation did not only affect the streamflow at the measurement point, but also the spatio-temporal runoff generated in the basin at pixel level. This could propagate to the water quality or plastic transport study. Among the datasets tested, CHIRPS emerged as the most reliable and suitable for hydrological modelling in this basin, both quantitatively and qualitatively.