EGU24-13564, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

SWEET (Snow Water Equivalent Estimation Tool): A new tool to generate updated SWE estimates for poorly monitored regions

Simone Schauwecker1, Álvaro Ayala1, Gonzalo Cortés1, Eduardo Yáñez1, Shelley MacDonell1,2, Katerina Goubanova1, and Cristian Orrego1
Simone Schauwecker et al.
  • 1CEAZA, La Serena, Chile (
  • 2Waterways Centre for Freshwater Management, Lincoln University and the University of Canterbury, Christchurch, New Zealand

In the dry Chilean North, the impact of the mountain snowpack on freshwater availability in the adjacent lowlands areas is crucial. The correlation between the snow water equivalent record and regionally averaged river discharges suggests that ~85% of the streamflow variance could be explained by the snowpack record alone. As seasonal snow cover depends on few winter events, there is a large year-to-year variability in the snow water equivalent (SWE). Typically, there are some dry years with very low annual precipitation which are compensated by wet years. However, since around 2010, the almost continuous extraordinarily dry conditions (so-called Central Chile “mega drought”) and increased water consumption in the region have led to significant stress on the water system. Hence, for an efficient water allocation and water management, it is crucial to know the actual SWE stored in the mountain snowpack. Until now, decisions have been based on scarce point measurements of the SWE or snow area estimates from MODIS. A drawback of these estimates is the large uncertainty that hampers efficient water allocation with important implications for water security of different sectors such as hydropower, agriculture and domestic use. 

To address this problem, we have developed a new operational SWE Estimation Tool for water resources decision making in the Coquimbo region (SWEET-Coquimbo), able to estimate current SWE in near real-time with a latency of ~10 days. SWE is estimated using a data assimilation framework that combines bias-corrected meteorological forcing ensembles from reanalysis data (ERA5, 5-day latency), hydrological modeling (Snowmodel) and satellite observations (Landsat) of the snow-covered area. SWEET-Coquimbo is placed in an open-access web platform, visualizing the current state of the SWE of five main catchments. The data can be downloaded and used for research and diagnostic purposes. 

The newly generated data show SWE for the period 2000-2023. We can now better understand the response of the regional snow cover to the Central Chile megadrought on snow cover and general trends in SWE over the last two decades. SWEET-Coquimbo has allowed, for the first time, a catchment-based estimation of the water available from the snowpack, which can now be used to improve seasonal runoff forecasts. Furthermore, our method has a great potential to be validated and applied to other mountainous regions with sparse in-situ data, as it does not rely directly on in-situ data.

How to cite: Schauwecker, S., Ayala, Á., Cortés, G., Yáñez, E., MacDonell, S., Goubanova, K., and Orrego, C.: SWEET (Snow Water Equivalent Estimation Tool): A new tool to generate updated SWE estimates for poorly monitored regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13564,, 2024.