Snow remote sensing applications for hydrological modeling and forecasting

Northern areas are subject to accelerated warming due to global climate change, with Arctic areas experiencing temperature increases up to four times compared to equatorial regions. Warming temperatures have resulted in changes to the duration and distribution of seasonal snow cover through earlier spring melt but also changing precipitation patterns in winter. Satellite data records indicate that the trends of the total mass of seasonal snow may vary greatly on different regions of the Northern Hemisphere, and the overall impact on surface water availability from seasonal snow is highly uncertain.

Due to limitations in the accuracy and spatial resolution of present operational sensors for snow, often passed on passive microwave radiometers, exploitation of snow information from satellite remote sensing in operational hydrology has been limited. The accuracy of available products in many areas is insufficient to meet the needs of e.g. operational runoff forecasts for hydropower. However, in anticipation of improved observational capacity from future sensors, investigating the potential impact of new snow products from upcoming sensors is highly relevant. Observing System Simulation Experiments (OSSEs) coupled with operational hydrology models can been used to assess the thresholds where satellite remote sensing products may bring added value to operational forecasts.

We are looking for a talented and motivated candidate to advance the use of remotely sensed properties of seasonal snow, in particular of Snow Water Equivalent, in hydrological models and forecasts. The PhD project aims at understanding ongoing changes in snow mass over northern areas, and how these impact freshwater availability in the future. The main objective of the project is to investigate and improve the uptake of present and upcoming snow mass estimates from satellite remote sensing in hydrology, including operational runoff forecasts, focusing on the following research questions: 1) investigation of the validity and limitations of present approaches based on passive microwave remote sensing from a hydrology viewpoint, making use of in situ observations, snow process models and other reference data sources 2) coupling existing remote sensing snow products and conducting OSSE-type experiments on anticipated snow products from future satellite sensors with hydrological models at basin scale investigating potential benefits and limitations in operational hydrology forecasts; 3) assessing the impact of anticipated changes in seasonal snow on availability of freshwater and hydropower capacity in the future, focusing on selected research basins.
The PhD project may also encompass a selection of the above research questions, based on interests and the background of the applicant. The work is conducted in the context of the Digital Waters (DIWA) flagship program. Work is done in close collaboration with larger research teams at FMI and University of Oulu.

The position requires an active and independent approach, diligence and cooperation skills, willingness to work as a part of a team and a passion to learn new. We also expect a Master’s degree in, for example, hydrology, meteorology, water and environmental engineering, physical geography or a related field of natural sciences. Due to the nature of the described PhD project, the candidate would benefit also from having e.g. the following skills: (1) Good understanding of snow physics and hydrological processes; (2) understanding of microwave remote sensing methods, instruments and data products (3) experience with numerical models including hydrological models (4) Good and demonstrated data analysis and numerical skills, and knowledge from programming languages such as R, Python, Matlab, etc. (5) capabilities and experience in handling large data volumes.

Finnish Meteorological institute

Water, Energy and Environmental Engineering Research Unit, Faculty of Technology

Pertti Ala-aho (UOULU)

Anna Kontu (FMI)

Juha Lemmetyinen (FMI)

nn.