Chapter 1: HBV.IANIGLA overview

Ezequiel Toum



Hydrological modeling is widely used by engineers, meteorologists, geographers, geologists and researchers interested in knowing, for example, the river runoff in the next few days, what will happen with the snowpack given certain changes in temperature or precipitation, among many other hydrological processes.

In the Andes of Argentina, water is an essential resource for crop irrigation, industrial and human water supply, hydropower generation and for the environmental balance. The important role of mountain watersheds and its relative high sensitivity to climate change (e.g.: in precipitation amounts) highlights the need of shedding light (unless) in some issues (Viviroli et al. 2011):

Despite the fact that Argentina has a mountain range of more than 3000 km long and large populations depending on the water generated along the Andes, there are just a few scientific studies focus on the Andes hydro-climatology. Among these studies, the works using hydrological modeling tools are scarce and none of them incorporate glaciers in the hydrological cycle (see Masiokas et al. (2020) for a review).

The HBV.IANIGLA was developed to solve hydroclimatic modeling problems in the Andes of Argentina, a mountain range with unique characteristics where other open access versions of the HBV (e.g.: HBV-Light, TUWmodel) are difficult to apply. As limitations of the previous versions we can mention the lack of a glacier module (TUWmodel), the limited number of elevation bands (HBV-light) and the impossibility to incorporate new modules, among others. The package was design with a modular approach and offers the possibility of applying it using diverse spatio-temporal scales with dissimilar objectives in the same environment (e.g.: real time streamflow forecasting, teaching hydrological models or simulating the surface mass balance of glaciers). It also offers the chance of combining the modules with other R-related hydrological packages (e.g.: Evapotranspiration, DEoptim, topmodel, hydromad) or with functions required by the user (Guo, Westra, and Peterson 2019; Ardia et al. 2016; Buytaert 2018).

Model structure

The model counts with the following modules:

The user will also find a precipitation and air temperature functions to extrapolate records to another heights.


Ardia, David, Katharine Mullen, Brian Peterson, and Joshua Ulrich. 2016. DEoptim: Global Optimization by Differential Evolution.
Buytaert, Wouter. 2018. Topmodel: Implementation of the Hydrological Model TOPMODEL in r.
Guo, Danlu, Seth Westra, and Tim Peterson. 2019. Evapotranspiration: Modelling Actual, Potential and Reference Crop Evapotranspiration.
Masiokas, M. H., A. Rabatel, A. Rivera, L. Ruiz, P. Pitte, J. L. Ceballos, G. Barcaza, et al. 2020. “A Review of the Current State and Recent Changes of the Andean Cryosphere.” Frontiers in Earth Science 8.
Viviroli, D., D. R. Archer, W. Buytaert, H. J. Fowler, G. B. Greenwood, A. F. Hamlet, Y. Huang, et al. 2011. “Climate Change and Mountain Water Resources: Overview and Recommendations for Research, Management and Policy.” Hydrol. Earth Syst. Sci. 15 (2): 471–504.