University of Lund
Hydrological Seasonal Forecasting.
Degree: 2019, University of Lund
In Sweden, almost half of the electricity produced
comes from hydropower. However, the amount of water in the
reservoir catchments is not evenly distributed throughout the year.
During the colder months, precipitation usually falls as snow and
accumulates into a snowpack. This frozen water is not available to
the energy producers until the spring snow melt when as much as 70%
of the annual discharge will be generated. This can create a
situation where there is a shortage of water resources during the
winter when demand and energy prices are high, and a surplus during
the spring and summer when demand and prices are lower. Hydropower
plant operators try to minimize this asymmetric distribution
through regulation of reservoir storages and hydrological forecasts
are crucial for this.However, the predominant method for
hydrological seasonal forecasting of the spring flood period in
Scandinavia is the Ensemble Streamflow Prediction (ESP) approach.
ESP uses historical observations of precipitation and temperature
from previous years (a so-called historical ensemble) to force the
hydrological model. The problem is that these forecasts are
climatological in character, i.e. it performs well when the weather
during the forecast period evolves normally, however if the
development of weather conditions is not "normal", the season
forecast will be more or less wrong.The thesis of this work is that
it is possible to improve seasonal forecasts so that they still
have skill even when the weather deviates from the normal climate
during the forecast period. By better understanding what affects
the variability in the hydrology and using that information to
inform how to modifying or replace the ESP forecasting approach, it
is possible to real skilful improvements over the ESP.In this work
it is shown that the variability in selected teleconnection
patterns are the leading source of variability in the seasonal
discharge. In the case of the spring flood period in northern
Sweden, these are the North Atlantic Oscillation, Arctic
Oscillation, and Scandinavian pattern. With the help of information
garnered by investigating these connections it is possible to
modify different forecast modelling chains, that on their own show
limited (if any) skill over ESP, and combine them into a
multi-chain forecast system that does show skill over the ESP. A
multi-model made up of three different individual modelling chains,
using a simple weighting scheme to combine them, is able to improve
the general skill of spring flood volume forecasts and improve
their ability to predict non-normal events
Subjects/Keywords: Teknik och teknologier; hydrological seasonal forecasting; seasonal forecast; spring flood; multi-model ensembles; teleconnection patterns; modelling chain
to Zotero / EndNote / Reference
APA (6th Edition):
Foster, K. (2019). Hydrological Seasonal Forecasting. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/bf1784c2-8d69-4f7a-8963-30fb26b59a25 ; http://portal.research.lu.se/ws/files/69071987/e_spikex_kean_nr2.pdf
Chicago Manual of Style (16th Edition):
Foster, Kean. “Hydrological Seasonal Forecasting.” 2019. Doctoral Dissertation, University of Lund. Accessed September 19, 2019.
http://lup.lub.lu.se/record/bf1784c2-8d69-4f7a-8963-30fb26b59a25 ; http://portal.research.lu.se/ws/files/69071987/e_spikex_kean_nr2.pdf.
MLA Handbook (7th Edition):
Foster, Kean. “Hydrological Seasonal Forecasting.” 2019. Web. 19 Sep 2019.
Foster K. Hydrological Seasonal Forecasting. [Internet] [Doctoral dissertation]. University of Lund; 2019. [cited 2019 Sep 19].
Available from: http://lup.lub.lu.se/record/bf1784c2-8d69-4f7a-8963-30fb26b59a25 ; http://portal.research.lu.se/ws/files/69071987/e_spikex_kean_nr2.pdf.
Council of Science Editors:
Foster K. Hydrological Seasonal Forecasting. [Doctoral Dissertation]. University of Lund; 2019. Available from: http://lup.lub.lu.se/record/bf1784c2-8d69-4f7a-8963-30fb26b59a25 ; http://portal.research.lu.se/ws/files/69071987/e_spikex_kean_nr2.pdf