Bertel Vehviläinen

A National-Scale Nutrient Loading Model for Finnish Watersheds—VEMALA

VEMALA is an operational, national-scale nutrient loading model for Finnish watersheds. It simulates hydrology; nutrient processes; leaching; and transport on land, rivers, and lakes. The model simulates nutrient gross load, retention, and net load from Finnish watersheds to the Baltic Sea. It was developed over a period of many years and three versions are currently operational, simulating different nutrients and processes. The first version of VEMALA (vs. 1.1) is based on a regression model between nutrient concentration and runoff. Since the first version, the model has been developed towards a more process-based nutrient loading model, by developing a catchment scale, semi-process-based model of total nitrogen loading, VEMALA-N, and by incorporating and developing a field-scale process-based model, ICECREAM, for total phosphorus loading simulations (VEMALA-ICECREAM). The model performance was tested in two ways: (1) by comparison of simulated net nitrogen and phosphorus loads with loads calculated from monitoring data for all major watersheds in Finland and (2) by comparing simulated and observed daily nutrient concentrations for the river Aurajoki by both old and new, process-based model approaches. Comparison of the results shows that the model is suitable for nutrient load simulation at a watershed scale and at a national scale; the new versions of the model are also suitable for applications at a smaller scale.

Effects of climate change and agricultural adaptation on nutrient loading from Finnish catchments to the Baltic Sea

Climate change is expected to increase annual and especially winter runoff, shorten the snow cover period and therefore increase both nutrient leaching from agricultural areas and natural background leaching in the Baltic Sea catchment. We estimated the effects of climate change and possible future scenarios of agricultural changes on the phosphorus and nitrogen loading to the Baltic Sea from Finnish catchments. In the agricultural scenarios we assumed that the prices of agricultural products are among the primary drivers in the adaptation to climate change, as they affect the level of fertilization and the production intensity and volume and, hence, the modeled changes in gross nutrient loading from agricultural land. Optimal adaptation may increase production while supporting appropriate use of fertilization, resulting in low nutrient balance in the fields. However, a less optimal adaptation may result in higher nutrient balance and increased leaching. The changes in nutrient loading to the Baltic Sea were predicted by taking into account the agricultural scenarios in a nutrient loading model for Finnish catchments (VEMALA), which simulates runoff, nutrient processes, leaching and transport on land, in rivers and in lakes. We thus integrated the effects of climate change in the agricultural sector, nutrient loading in fields, natural background loading, hydrology and nutrient transport and retention processes.

Data assimilation and parametrisation of lakes in HIRLAM

ABSTRACT When the resolution of numerical weather prediction (NWP) and climate models increases, it becomes more and more important to correctly account for the lake–atmosphere interactions. One possible way to handle lake effects is to use a lake model, which treats the lake surface temperature and ice conditions as prognostic variables. Such a parametrisation eliminates the traditional for NWP need to prescribe lake characteristics based on long-term climate averages. At the same time, new in situ and satellite measurements are becoming available for an operational practice. This offers the possibility to assimilate lake observations into the NWP models. We study the applicability of the prognostic and observation-based approaches and compare both. As a first step towards integrated lake data assimilation and forecasting in NWP, we suggest using the results of the prognostic lake parametrisation as the background for objective analysis (spatialisation) of the lake water surface temperature observations. We run NWP experiments in the Nordic conditions, where the freezing and melting of lakes can significantly influence local weather. Our results indicate that a lake model, usually used in climate studies, works well also in the NWP model even without assimilation of observations. However, it is possible to improve the description of the changing lake surface state by using good observation data. In this case, the lake model provides a better background for the data assimilation than a lake surface temperature climatology.

The Influence of Climate Change on Energy Production & Heating Energy Demand in Finland

In this study we have examined how the anticipated anthropogenic climate change will affect the heating power demand of buildings, hydropower production, the climatological potential of peat production, bioenergy, and wind energy. The study concentrates on conditions in Finland and the future period studied was 2021–2050. The future climate conditions were primarily taken from simulations by the Hadley Centres global climate model, HadCM3. According to the climate scenarios used in this study, the heating energy demand for the period 2021–2050 will decrease on average by some 10 % from the period 1961–1990. At the same time hydropower production will increase by 7–11 %, the climatological potential of peat production by 17–24 %, the climatological potential of biomass (mainly wood) by 10–15 % and the climatological potential of wind power by 2–10 %. These results must still be considered as preliminary, mainly because there are still large uncertainties related to the estimation of the magnitude of climate change.

Generation of Soil Moisture Maps from ENVISAT/ASAR images in a Finland area by using a Neural Network Algorithm

A Neural Network algorithm was tested in two Italian sites for producing multi-temporal soil moisture maps starting from ENVISAT/ASAR images, collected in 2003 and 2004. Several SAR images were analyzed for a flat agricultural area located close to Alessandria in North-west Italy, and a mountainous site on the Italian Alps. The obtained results showed a reasonable agreement with ground truth data and meteorological conditions, and maps with 4-5 levels of soil moisture of both the test sites were generated from the available ENVISAT ASAR images. A further validation of the retrieval algorithm was carried out by using two ENVISAT images collected form the Kemjoki River beat bog area in Finland, on May 7, 2004 and July 27, 2005. In spite of the problems related to the wideness and non-homogeneity of the area, and the lack of detailed ground measurements, the results obtained by using the Artificial Neural Network can be considered satisfactory. Soil moisture is a key state variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. We know that local measurements of soil moisture content (SMC) are strongly affected by spatial variability, besides being time-consuming and expensive. Moreover, the use of hydrological models for extending the forecast of soil moisture over larger areas is not easy, and depends on the homogeneity of the selected areas and the information available on them (soil properties, i.e. hydraulic characteristics, and permeability, together with meteorological and climatological data, etc.). The possibility of measuring soil moisture on a large scale from satellite sensors, with complete and frequent coverage of the Earths surface, is, therefore, extremely attractive. However, up to now, the only available frequency from space is C band, operational on ERS-2, RADARSAT, and ENVISAT satellites, which is not the optimal for this aim. The retrieval of soil moisture maps at C-band is, in fact, still challenging, since the effects of soil surface roughness and vegetation cover on the backscattering coefficient at this frequency is high, and needs the use of correcting procedures (1-3). In spite of these problems, multi-temporal soil moisture maps have been already produced in two Italian sites starting from ENVISAT/ASAR images, collected in 2003 and 2004, by using an algorithm based on Neural Networks. In this case several SAR images were analyzed for a flat agricultural area, located in North-west Italy, and a mountainous site on the Italian Alps. The obtained results showed a satisfactory agreement with ground truth data and meteorological conditions, and enabled us to generate maps with 4-5 levels of soil moisture of both the test sites from the available ENVISAT ASAR images (4). Using ENVISAT data collected on the Kemijoki area in Finland, a further validation of the retrieval algorithm was carried out. In spite of the problems related to the difference in polarization configuration, the wideness and non-homogeneity of the area, and the lack of detailed ground measurements, the results obtained by using the Artificial Neural Network were satisfactory.

Improving hydrological forecasting using multi-source remote sensing data together with in situ measurements

This paper describes the development of information systems and techniques for improving hydrological forecasting by applying satellite observations, weather radars, and in situ measurements from automatic monitoring stations. In the methodology developed and demonstrated, the observation data are accompanied with a detailed soil and land cover information. The information system is concerned with the following physical characteristics relevant to river discharges and flooding: snow water equivalent (SWE), cumulative amount of precipitation, fraction of snow covered area during the melting period (FSC), soil moisture, and soil frost. Feasibility of the multi-source information system is demonstrated in a pilot experiment for Finnish Lapland, using the hydrological forecasting system of the Finnish Environment Institute (SYKE) as an example of a typical operational distributed model.