Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.

An intercomparison is made of the Net Ecosystem Exchange of CO 2 , NEE, for eight Dutch grassland sites: four natural grasslands, two production grasslands and two meteorological stations within a rotational grassland region. At all sites the NEE was determined during at least 10 months per site, using the eddy-covariance (EC) technique, but in different years. The NEE does not include any import or export other than CO 2. The photosynthesis-light response analysis technique is used along with the respiration-temperature response technique to partition NEE into Gross Primary Production (GPP) and Ecosystem Respiration (R e) and to obtain the eco-physiological characteristics of the sites at the field scale. Annual sums of NEE, GPP and R e are then estimated using the fitted response curves with observed radiation and air temperature from a meteorological site in the centre of The Netherlands as drivers. These calculations are carried out for four years (2002-2005). Land use and management histories are not considered. The estimated annual R e for all individual sites is more or less constant per site and the average for all sites amounts to 1390±30 gC m −2 a −1. The narrow uncertainty band (±2%) reflects the small differences in the mean annual air temperature. The mean annual GPP was estimated to be 1325 g C m −2 a −1 , and displays a much higher standard deviation, of ±110 gC m −2 a −1 (8%), which reflects the relatively large variation in annual solar radiation. The mean annual NEE amounts to-65±85 gC m −2 a −1. From two sites, four-year records of CO 2 flux were available and analyzed (2002-2005). Using the weather record of 2005 with optimizations from the other years, the standard deviation of annual GPP was estimated to be 171-206 gC m −2 a −1 (8-14%), of annual R e 227-247 gC m −2 a −1 (14-16%) and of annual NEE 176-276 gC m −2 a −1. The Correspondence to: C. M. J. Jacobs (cor.jacobs@wur.nl) inter-site standard deviation was higher for GPP and R e , 534 gC m −2 a −1 (37.3%) and 486 gC m −2 a −1 (34.8%), respectively. However, the inter-site standard deviation of NEE was similar to the interannual one, amounting to 207 gC m −2 a −1. Large differences occur due to soil type. The grasslands on organic (peat) soils show a mean net release of CO 2 of 220±90 g C m −2 a −1 while the grasslands on mineral (clay and sand) soils show a mean net uptake of CO 2 of 90±90 g C m −2 a −1. If a weighing with the fraction of grassland on organic (20%) and mineral soils (80%) is applied, an average NEE of 28 ±90 g C m −2 a −1 is found. The results from the analysis illustrate the need for regionally specific and spatially explicit CO 2 emission estimates from grassland.

Abstract. An intercomparison is made of the Net Ecosystem Exchange of CO 2 , NEE, for eight Dutch grassland sites: four natural grasslands, two production grasslands and two meteorological stations within a rotational grassland region. At all sites the NEE was determined during at least 10 months per site, using the eddy-covariance (EC) technique, but in different years. The NEE does not include any import or export other than CO 2 . The photosynthesis-light response analysis technique is used along with the respiration-temperature response technique to partition NEE into Gross Primary Production (GPP) and Ecosystem Respiration (R e ) and to obtain the eco-physiological characteristics of the sites at the field scale. Annual sums of NEE, GPP and R e are then estimated using the fitted response curves with observed radiation and air temperature from a meteorological site in the centre of The Netherlands as drivers. These calculations are carried out for four years (2002)(2003)(2004)(2005). Land use and management histories are not considered. The estimated annual R e for all individual sites is more or less constant per site and the average for all sites amounts to 1390±30 gC m −2 a −1 . The narrow uncertainty band (±2%) reflects the small differences in the mean annual air temperature. The mean annual GPP was estimated to be 1325 g C m −2 a −1 , and displays a much higher standard deviation, of ±110 gC m −2 a −1 (8%), which reflects the relatively large variation in annual solar radiation. The mean annual NEE amounts to -65±85 gC m −2 a −1 . From two sites, four-year records of CO 2 flux were available and analyzed (2002)(2003)(2004)(2005). Using the weather record of 2005 with optimizations from the other years, the standard deviation of annual GPP was estimated to be 171-206 gC m −2 a −1 (8-14%), of annual R e 227-247 gC m −2 a −1 (14-16%) and of annual NEE 176-276 gC m −2 a −1 . TheCorrespondence to: C. M. J. Jacobs (cor.jacobs@wur.nl) inter-site standard deviation was higher for GPP and R e , 534 gC m −2 a −1 (37.3%) and 486 gC m −2 a −1 (34.8%), respectively. However, the inter-site standard deviation of NEE was similar to the interannual one, amounting to 207 gC m −2 a −1 . Large differences occur due to soil type. The grasslands on organic (peat) soils show a mean net release of CO 2 of 220±90 g C m −2 a −1 while the grasslands on mineral (clay and sand) soils show a mean net uptake of CO 2 of 90±90 g C m −2 a −1 . If a weighing with the fraction of grassland on organic (20%) and mineral soils (80%) is applied, an average NEE of 28 ±90 g C m −2 a −1 is found. The results from the analysis illustrate the need for regionally specific and spatially explicit CO 2 emission estimates from grassland.

Landscape representations based on land cover databases differ significantly from the real landscape. Using a land cover database with high uncertainty as input for emission inventory analyses can cause propagation of systematic and random errors. The objective of this study was to analyze how different land cover representations introduce systematic errors into the results of regional N2O emission inventories. Surface areas of grassland, ditches, and ditch banks were estimated for two polders in the Dutch fen meadow landscape using five land cover representations: four commonly used databases and a detailed field map, which most closely resembles the real landscape. These estimated surface areas were scaled up to the Dutch western fen meadow landscape. Based on the estimated surface areas agricultural N2O emissions were estimated using different inventory techniques. All four common databases overestimated the grassland area when compared to the field map. This caused a considerable overestimation of agricultural N2O emissions, ranging from 9% for more detailed databases to 11% for the coarsest database. The effect of poor land cover representation was larger for an inventory method based on a process model than for inventory methods based on simple emission factors. Although the effect of errors in land cover representations may be small compared to the effect of uncertainties in emission factors, these effects are systematic (i.e., cause bias) and do not cancel out by spatial upscaling. Moreover, bias in land cover representations can be quantified or reduced by careful selection of the land cover database.

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