Parsimonious Modeling of Snow Accumulation and Snowmelt Processes in High Mountain Basins

Abstract

The success of hydrological modeling of a high mountain basin depends in most case on the accurate quantification of the snowmelt. However, mathematically modeling snowmelt is not a simple task due to, on one hand, the high number of variables that can be relevant and can change significantly in space and, in the other hand, the low availability of most of them in practical engineering. Therefore, this research proposes to modify the original equation of the classical degree-day model to introduce the spatial and temporal variability of the degree-day factor. To evaluate the effects of the variability in the hydrological modeling and the snowmelt modeling at the cell and hillslope scale. We propose to introduce the spatial and temporal variability of the degree-day factor using maps of radiation indices. These maps consider the position of the sun according to the time of year, solar radiation, insolation, topography and shaded-relief topography. Our priority has been to keep the parsimony of the snowmelt model that can be implemented in high mountain basins with limited observed input. The snowmelt model was included as a new module in the TETIS distributed hydrological model. The results show significant improvements in hydrological modeling in the spring period when the snowmelt is more important. At cell and hillslope scale errors are diminished in the snowpack, improving the representation of the flows and storages that intervene in high mountain basins.

Coupled Model for Assessing the Present and Future Watershed Vulnerabilities to Climate Change Impacts

“…Hydrological modeling was conducted using the TETIS model (see [45,46] for more information about the TETIS model) and daily flow data were measured at the Las Adjuntas gauge, which is located at the mouth of the sub-basin (Figure 3). The flow data measured can be freely downloaded from the website of CONAGUA National Data Bank of Surface Waters (https://www.imta.gob.mx/bandas accessed on 8 February 2023).…”

“…The NSE serves as the objective function of the hydrological model calibration [47] as it normalizes the model performance into an interpretable scale [48]. Table 2 presents the average of the effective parameters resulting from the calibration process [45,46,49]. In terms of efficiency, Figure 4a shows that the model reaches a NSE of 0.70 for the calibration period.…”

Modelado de la producción de sedimentos en una cuenca con poca información incluyendo los potenciales efectos del cambio climático y el cambio de uso de suelo

“…Los nueve parámetros son: almacenamiento estático, índice de cobertura vegetal, capacidad de infiltración, velocidad de la escorrentía directa, capacidad de percolación, interflujo, flujo subterráneo profundo, flujo base y velocidad en la red fluvial (Orozco, Martínez & Ortega, 2020). Estos nueve parámetros han sido calibrados de forma manual y automática a través de factores correctores (FC) (Orozco, Francés & Mora, 2019;Orozco, Ramírez & Francés, 2018;Orozco et al, 2020). La calibración automática se ha realizado usando el algoritmo de optimización Shuffled Complex Evolution de la Universidad de Arizona (SCE-UA) propuesto por Duan, Sorooshian & Gupta (1992), y se ha empleado como función objetivo el índice de eficiencia de Nash-Sutcliffe (NSE, por sus siglas en inglés), el cual es el más usado en la calibración de modelos hidrológicos y emplea la ecuación siguiente:…”