Studies on long-term inflow forecasting

This thesis aims to improve knowledge of long-term inflow and streamflow forecasts. A special focus is on the development of a new long-term forecast model and on the evaluation of long-term inflow forecasts. In the first part of the work, a new categorical long-term forecast model is developed and its performance is investigated in four case studies. The forecasts are based only on the current hydrological state of the basin and thus, weather forecasts are not utilised. By using the k-Nearest Neighbour Rule (k-NRR) or the minimum distance classifier (MDC), the forthcoming period is classified into a wetness class based on the hydrological state of the basin on the forecast date. Inflow forecast is finally based on this classification. The results show that for a lake with a large basin (Lake Päijänne case study), this forecast model could be used in real-time inflow forecasting and the results are comparable with the forecast accuracy of the multiple linear regression models. For small basins (<10 km²) and in Lake Pyhäjärvi, the use of the new model for long-term discharge forecasting gave satisfactory results on April 1. On October 1, long-term forecasting turned out to be difficult irrespective of the forecast model. In the second part of the work, long-term inflow forecasts are evaluated based on their length and accuracy. The study is based on two cases: a single multipurpose reservoir Lake Pyhäjärvi in Säkylä and a multipurpose lake-river system, River Kymijoki. The evaluation method is based on artificially generated inflow forecasts and on the optimisation of the release sequences based on these forecasts. The results are in line with the outcome of similar international studies: if the live capacity of the lake-river system compared with the annual inflow is small, short and accurate forecasts should be aimed at. For large systems, a long forecast period should be used without focusing as much on forecast accuracy. The main finding, however, is related to approximation of the potential hydropower production increase in Finland by supposing that forecast accuracy could be improved and the optimal forecast periods used. In the two case studies it was possible to increase hydropower production up to 0.7-9% compared with the status quo during the study period, if perfect inflow forecasts had been available. However, the realistic possibilities to increase hydropower production in Finland by improving forecast accuracy were approximated to be 0.5-2% at the maximum. At the same time problems related to floods and droughts would decrease. Simulated annealing is used as the optimisation algorithm in the operation of the systems, and the evaluation of the performance of this algorithm was one of the special objectives of this study. The algorithm was flexible and reliable

Advanced representation of the ocean/sea ice dynamics at high latitudes

Sea ice is a fundamental element of global climate system, with numerous impacts on the polar environment. The ongoing drastic changes in the Earth’s sea ice cover highlight the necessity of monitoring the polar regions and systematically evaluating the quality of different numerical products. The main objective of this thesis is to improve our knowledge of the representation of Arctic and Antarctic sea ice using comprehensive global ocean reanalyses and coupled climate models. The dissertation will explore (i) the Antarctic marginal ice zone (MIZ) and pack ice area in the ensemble mean of four global ocean reanalyses called GREP; (ii) historical representation of the Arctic and Antarctic sea ice state in HighResMIP models; (iii) the future evolution of Arctic sea ice in HighResMIP models. Global ocean reanalyses and GREP are found to adequately capture interannual and seasonal variability in both pack ice and MIZ areas at hemispheric and regional scales. The advantage of the ensemble-mean approach is proved as GREP smooths the strengths and weaknesses of single systems and provides the most consistent and reliable estimates. This work is intended to encourage the use of GREP in a wide range of applications. The analysis of sea ice representation in the coupled climate models shows no systematic impact of the increased horizontal resolution. We argue that a few minor improvements in sea ice representation with the enhanced horizontal resolution are presumably not worth the major effort of costly computations. The thesis highlights the critical importance to distinguish the MIZ from consolidated pack ice both for investigating changes in sea ice distribution and evaluating the product’s performance. Considering that the MIZ is predicted to dominate the Arctic sea ice cover, the model physics parameterizations and sea ice rheology might require modifications. The results of the work can be useful for modelling community

Forecast verification of a 3D model of the Mediterranean Sea. Analysis of model results and observations using wavelets and Empirical Orthogonal Functions.

The quality assessment of the three-dimensional GHER (GeoHydrodynamics and Environmental Research) model of the Mediterranean Sea is presented in this work. The verification of the model results is done in a spatio-temporal approach. Traditional error measures (i.e. correlation, mean error, etc) are very useful to assess the quality of a model, but they do not take into account the high complexity of three-dimensional models. The verification process is thus done in three main parts: first, the model is compared to observations and climatology in a qualitative approach, in order to make a preliminar study about the model behaviour. Then, the error assessment is done, using traditional statistic measures. In order to take into account the complexity of the model and observations, the last step in the verification process consists in a spatio-temporal analysis using wavelets and empirical orthogonal functions. This last analysis will allow us to have an insight about the model quality in a more detailed way. This verification process has been applied to the GHER model. This model is implemented in a two-way nesting approach in the Mediterranean Sea, Liguro-Provençal basin and Ligurian Sea, where the highest resolution is reached. Assimilation of sea surface temperature and sea level anomaly is made during a nine-week experiment. Another test is carried out, to assess the quality of sea surface temperature from the SOFT predictor of the Ligurian Sea. The predicted sea surface temperature is assimilated in the model and the quality of the forecast is compared to the first assimilation experiment. The assimilation of the SOFT statistical predictors can be very useful to force models in a real forecast experiment, where no observations are available