Markus Huttunen

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.

Detecting the onset of snow-melt using SSM/I data and the self-organizing map

In this paper, we present an algorithm to estimate the onset of seasonal snow‐melt using space‐borne microwave radiometer data. We have earlier developed a simple model called a Channel Difference Algorithm (CDA) to estimate the beginning of the snow‐melt. The new algorithm, the SOM Detection Algorithm (SDA), is based on the use of an artificial neural network system a called Self‐Organizing Map (SOM). The purpose of this research is to develop a robust and simple algorithm feasible for operative use. The algorithm is tested using SSM/I data with hydrological predictions as reference data. The reference data covers two winters, 1997 and 1998, and is for the boreal forest zone in Finland. The results are promising. The SDA is able to estimate the beginning of the final snow‐melt well, especially if the snow water equivalent exhibits large values. Using low‐pass filtering for the SDA estimated time series, the estimation can be improved.

ESLab application to a boreal watershed in southern Finland: preparing for a virtual research environment of ecosystem services.

Abstract We report on preparatory work to develop a virtual laboratory for ecosystem services, ESLab, and demonstrate its pilot application in southern Finland. The themes included in the pilot are related to biodiversity conservation, climate mitigation and eutrophication mitigation. ESLab is a research environment for ecosystem services (ES), which considers ES indicators at different landscape scales: habitats, catchments and municipalities and shares the results by a service that utilizes machine readable interfaces. The study area of the pilot application is situated in the boreal region of southern Finland and covers 14 municipalities and ten catchments including forested, agricultural and nature conservation areas. We present case studies including: present carbon budgets of natural ecosystems; future carbon budgets with and without the removal of harvest residues for bioenergy production; and total phosphorus and nitrogen future loads under climate and agricultural yield and price scenarios. The ESLab allows researchers to present and share the results as visual maps, statistics and graphs. Our further aim is to provide a toolbox of easily accessible virtual services for ES researchers, to illustrate the comprehensive societal consequences of multiple decisions (e.g. concerning land use, fertilisation or harvesting) in a changing environment (climate, deposition).

Estimation of snow covered area by applying apparent regional transmissivity

The detection of snow from optical instruments is often hampered by forest canopy. In this paper, an empirical reflectance model for estimating regional values for snow covered area (SCA) from optical data is presented. In the model, SCA is expressed as a function of apparent vegetation transmissivity. The estimation of SCA has been tested for NOAA/AVHRR data with drainage basins as calculation units. The same areas are used in an operative hydrological model. Comparison of estimated SCA with reference data indicates good correlation.

Mapping of Snow Water Equivalent and Snow Coverage from Combined EO and in situ Data for Climatic Studies and Hydrological Forecasting Models

Information on physical snow cover characteristics, such as snow water equivalent (SWE) and the areal coverage fraction of snow covered area (SCA), can be obtained from space-borne remote sensing data. The feasible instruments include optical spectrometers and microwave radars (SCA mapping), and microwave radiometers (SWE mapping). As data assimilation techniques are applied, the EO data-derived information can improve the performance of river discharge forecasting models and the knowledge on snow climatology. The results discussed here indicate that the assimilation of EO data-based SCA estimates to hydrological modeling significantly improves the accuracy of operational river discharge forecasts. The results also indicate that the employment of space-borne microwave radiometer data using the data assimilation technique improves the SWE or snow depth mapping accuracy when compared with the use of values interpolated from synoptic observations.

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.

Estimation of snow pack characteristics and snow covered area in boreal forests from ERS-2 SAR and Envisat ASAR data

Estimation of snow moisture (total liquid water content) and the fraction of snow covered area (SCA) are investigated by applying multi-year ERS-2 SAR and Envisat ASAR data sets. An inversion approach for the moisture retrieval is introduced. The results suggest that C-band radar is operationally feasible for both applications

Assimilation of SAR data to operational hydrological runoff and snow melt forecasting model

Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bayesian techniques is demonstrated and tested. The developed assimilation technique combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from four springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.

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.