The effect of satellite derived leaf area index and roughness length information on modelled reactive nitrogen deposition in north-western Europe

The nitrogen cycle has been continuously disrupted by human activity over the past century, resulting in almost a tripling of the total reactive nitrogen fixation in Europe. Consequently, excessive amounts of reactive nitrogen (Nr) have manifested in the environment, leading to a cascade of adverse effects, such as acidification and eutrophication of terrestrial and aquatic ecosystems, and particulate matter formation. Chemistry transport models 15 (CTM) are frequently used as tools to simulate the complex chain of processes that determine atmospheric Nr flows. In these models, the parameterization of the atmosphere-biosphere exchange of Nr is largely based on few surface exchange measurement and is therefore known to be highly uncertain. In addition to this, the input parameters that are used here are often fixed values, only linked to specific land use classes. In an attempt to improve this, a combination of multiple satellite products is used to derive updated, time-variant leaf area index (LAI) and 20 roughness length (z0) input maps. As LAI, we use the MODIS MCD15A2H product. The monthly z0 input maps presented in this paper are a function of satellite-derived NDVI values (MYD13A3 product) for short vegetation types (such as grass and arable land) and a combination of satellite-derived forest canopy height and LAI for forests. The use of these growth-dependent satellite products allows us to represent the growing season more realistically. For urban areas, the z0 values are updated, too, and linked to a population density map. The approach to derive these 25 dynamic z0 estimates can be linked to any land use map and is as such transferable to other models. We evaluated the resulting changes in modelled deposition of Nr components using the LOTOS-EUROS CTM, focusing on Germany, the Netherlands and Belgium. The implementation of these updated LAI and z0 input maps led to local changes in the total Nr deposition of up to ~30% and a general shift from wet to dry deposition. The most distinct changes are observed in land use specific deposition fluxes. These fluxes may show relatively large deviations, 30 locally affecting estimated critical load exceedances for specific natural ecosystems. 1 https://doi.org/10.5194/gmd-2019-256 Preprint. Discussion started: 1 October 2019 c © Author(s) 2019. CC BY 4.0 License.


Introduction
The nitrogen (N) cycle has been continuously disrupted by human activity over the past century (Fowler et al., 2015;Galloway et al., 2004;Galloway et al., 2008), resulting in a doubling of the total reactive nitrogen (N r ) fixation 35 globally and even a tripling in Europe. As a result, excessive amounts of N r , defined as all N compounds except N 2 , have manifested in the environment contributing to acidification and eutrophication of sensitive terrestrial and aquatic ecosystems (Bobbink et al., 2010a;Paerl et al., 2014). NO x and NH 3 affect air quality through their significant role in the formation of particulate matter, impacting human health and life expectancy (Lelieveld et al., 2015;Bauer et al., 2016;Erisman and Schaap, 2004). N r also influences climate change through its impact on 40 greenhouse gas emissions and radiative forcing Butterbach-Bahl et al., 2011). As N r forms are linked through chemical and biological conversion in one another within the environmental compartments, one atom of N may even take part in a cascade of N r forms and effects (Galloway et al., 2003). To minimize these adverse effects, effective nitrogen management and policy development, therefore, require consideration of all N r forms simultaneously.

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With the scarceness and inadequate distribution of available ground measurements, especially for reduced N r , the most important method to assess and quantify total N r budgets on a larger spatial scale to date remains the use of models. Models, chemistry transport models, in particular, are used for understanding the atmospheric transport and the atmosphere-biosphere exchange of nitrogen compounds. Most chemistry transport models compare reasonably with observations for oxidized forms of N r , but need improvement when it comes to the reduced forms of N r 50 (Colette et al., 2017). Modelled NH 3 fields are in general uncertain due to the highly reactive nature and the uncertain lifetime of NH 3 in the atmosphere. More importantly, NH 3 emissions that are used as model input are very complex to estimate and remain highly uncertain (Reis et al., 2009;Behera et al., 2013), for example, due to the diversity in NH 3 volatilization rates originating from different agricultural practices. Recently, a lot of effort has been made to improve the spatiotemporal distributions of bottom-up NH 3 emissions (e.g. (Hendriks et al., 55 2016;Skjøth et al., 2011). Emissions can also be estimated top-down through the usage of data assimilation and inversion techniques. Optimally combining observations and chemistry transport models have already enabled us to create large-scale emission estimates for various pollutants (e.g. (Curier et al., 2014;Abida et al., 2017), for instance for NO 2 , and will likely also be used for large-scale NH 3 emission estimates in the future.
Most data assimilation and inversion methods rely on the assumption that sink terms in the model hold a negligible 60 uncertainty. To obtain reasonable top-down emission estimates, we must thus also aim to reduce the uncertainty involved on this side of models. The sink strengths of trace gases and particles in chemistry transport models are often pragmatic and computed with relatively simple empirical functions (e.g. following (Wesely, 1989;Emberson et al., 2000;Erisman et al., 1994)), mostly linked to land use classification maps. The parameterization of the atmosphere-biosphere exchange of N r components that is used in models is largely based on surface exchange differences in the used input variables. Here, we focus on the leaf area index (LAI) and the roughness length (z 0 ) input values. The deposition velocity is often parameterized using both the LAI and the z 0 . Currently, most models 70 use fixed, land use specific values for both parameters. In practice, however, spatial as well as seasonal variation is observed. In this paper, we aim to improve the deposition flux modelling by using more realistic, spatial-and timevariant LAI and z 0 values that are derived from optical remote sensors.
The LAI is defined as the one-sided green leaf area per unit surface area (Watson, 1947). The LAI serves as a measure for the amount of plant canopy, and herewith directly related to energy and mass exchange processes. As a 75 result, the LAI is nowadays used as one of the main parameters in many ecological models. In deposition modelling, stomatal uptake is often parameterised using the LAI. The LAI can be determined in the field using either direct methods, such as leaf traps, or indirect methods, such as hemispherical photography (Jonckheere et al., 2004).
Another group of indirect methods to estimate the LAI is the use of optical remote sensing. The LAI can, for instance, be estimated using empirical relationships between LAI and vegetation indices (e.g. (Soudani et al., 80 2006;Davi et al., 2006;Turner et al., 1999) or by inversion of canopy reflectance models (e.g. (Houborg and Boegh, 2008;Myneni et al., 2015). A well-known example of the latter is the LAI product from the MODIS instrument, which we will use in this study.
The z 0 is used to describe the surface roughness. The surface roughness serves as a momentum sink for atmospheric flow and is, therefore, an important term in atmospheric modelling. The interaction between the boundary layer and 85 the roughness of the Earth's surface results in shear stress that affects the wind speed profile. Under neutral conditions, the resulting logarithmic wind profile is defined as: ( ) = * (eq. 1) ( ) represents the mean wind speed, * the friction velocity and the Von Kármán constant. Here, is a constant that represents the height at which the wind speed theoretically becomes zero. The z 0 can be estimated from 90 in-situ wind speed measurements using bulk transfer equations. More recently, several studies have shown that z 0 for specific, uniform land cover types can also be estimated from optical remote sensing measurements, for instance using vegetation indices (e.g. (Xing et al., 2017;Yu et al., 2016;Bolle and Streckenbach, 1993;Hatfield, 1988;). The z 0 can also be estimated using (satellite-derived) vegetation height (e.g. (Raupach, 1994;Plate, 1971;Brutsaert, 2013;Schaudt and Dickinson, 2000)).

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The use of optical remote sensing data holds promising potential for improvements of the representativeness of the surface characterization in chemistry transport models. Here, we illustrate the effect of replacing the default implementation with new, satellite-based LAI and z 0 maps on transport and deposition modelling of N r components.
We evaluate the changes in N r deposition and distribution fields, focusing on Germany, the Netherlands and Belgium. Moreover, we quantify and present the implications for land use specific fluxes on a model subpixel level.

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Also, we compare our model outputs with wet deposition measurements of NH 4 + and NO 3 and surface concentration measurements of NH 3 and NO 2 .

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The LOTOS-EUROS model is a Eulerian chemistry transport model that simulates air pollution in the lower troposphere (Manders et al., 2017). In this study the horizontal resolution is set to 0.125⁰ by 0.0625⁰, corresponding to pixels of approximately 7 by 7 kilometres in size. The model uses a five-layer vertical grid that extends up 5 km above sea level, starting with a surface layer with a fixed height of 25 meters. The next layer is a mixing layer, followed by two time-varying dynamic reservoir layers of equal thickness, and a top layer up to 5 km. LOTOS-

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EUROS follows the mixed layer approach and performs hourly results using ECMWF meteorology (European Centre for Medium-Range Weather Forecasts, 2016). The gas-phase chemistry uses the TNO CBM-IV scheme (Schaap et al., 2009) and the anthropogenic emissions from the TNO-MACC-III emission database (Kuenen et al., 2014). LOTOS-EUROS makes use of the CORINE/Smiatek land use map to determine input values for surface variables.

Dry deposition module
The dry deposition flux of gases is computed following the resistance approach, in which the exchange velocity is equal to the reciprocal sum of the aerodynamic resistance , the quasi-laminar boundary layer resistance and the canopy resistance : = (eq. 1) 120 (z , … ) is computed using stability parameters and the function proposed by Businger et al. (1971). (z , … ) follows the parameterization presented in McNaughton and Van Den Hurk (1995). and are both influenced by the wind profile. The wind profile, in turn, depends on the z 0 associated with different land use classes. An extensive description of the calculation of and can be found in Manders-Groot et al. (2016

Datasets
The following section gives a short description of all the datasets that are used in this study. Firstly, a description of the LAI dataset is given. Subsequently, the datasets that are used to derive the updated z 0 maps are described.
Finally, the in-situ observations that are used for the validation of the modelled N r deposition and concentration 140 fields are discussed in the last paragraph.

MCD15A2H Leaf Area Index
The satellite-derived Leaf Area Index (LAI) is a combined product of the MODIS instruments on board the Terra and Aqua satellites (Myneni et al., 2015). The LAI algorithm compares bidirectional spectral reflectances observed by MODIS to values evaluated with a vegetation canopy radiative transfer model that are stored in a look-up table.

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The algorithm then archives the mean and the standard deviation of the derived LAI distribution functions. We used the 6 th version of the product, MCD15A2H, which has a temporal resolution of 8 days and a spatial resolution of 500 meters.

MYD13A3 NDVI
The Normalized Difference Vegetation Index (NDVI) is a vegetation index computed with reflectances observed by 150 the MODIS instrument on board of the Aqua satellite (Didan, 2015). The NDVI is the normalized transform of the near infrared to the red reflectance and is expressed by: We used the MYD13A3 product, which is the monthly NDVI product with a spatial resolution of 1 km.

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The forest canopy height is derived from LIDAR (Light Detection And Ranging) data acquired by the GLAS (Geoscience Laser Altimeter System) instrument aboard the ICESat (Ice, Cloud, and land Elevation Satellite) satellite (Zwally et al., 2002). This instrument was an altimeter that transmitted a light pulse of 1024 nm and recorded the reflected waveform. We used the global forest canopy height product developed by Simard et al.
(2011), which has a spatial resolution of 1 km.

Population density map
The population density grid used in this study available for all European countries and provided by the European Environmental Agency (Gallego, 2010). The population density is disaggregated with the CORINE Land Cover   We only used background stations. The location of these stations can be found in Figure 18.

Updated z 0 maps
The updated z 0 maps are a composition of z 0 values derived using different methods. We distinguish three different 180 main approaches: 1) z 0 values that depend on forest canopy height, 2) z 0 values that depend on the NDVI and 3) new z 0 values for urban areas that depend on the population density map. In addition to these three approaches, the z 0 values of some urban classes were set to new default values. An overview of the datasets that are used for each DEPAC land use class is given in Table 1.
The MODIS NDVI, the MODIS LAI and the GLAS forest canopy height had to be pre-processed and homogenized 185 in order to obtain consistent input maps that can be read into the LOTOS-EUROS model. To achieve this, we created input maps for each DEPAC class on the coordinate grid of the CORINE/Smiatek land use map in LOTOS-

EUROS.
First of all, the original datasets were re-projected to geographic coordinates. To deal with the different horizontal resolution of these datasets the CLC2012 map, having the highest horizontal resolution, was used as a basis for the 190 computation of the updated z 0 values. The first step was to isolate homogeneous pixels within each dataset, which we defined as pixels that consist of over 85% of one individual CORINE land cover class. For each pixel of the datasets, we computed the percentages of each CORINE land cover class within that pixel. The pixels with percentages higher than 85% were isolated for each CORINE land cover class. We used the remaining pixels to compute z 0 values for each CORINE land cover class. The methods that were applied are described in the 195 subsequent section.

Forest canopy height derived z 0 values
The forest canopy height dataset derived from GLAS LiDAR observations is used to compute the z 0 values for each CLC2012 forest land cover class (broad-leaved forest, coniferous forest and mixed forest) that corresponds to one of the DEPAC forest land use classes (4: coniferous forest and 5: deciduous forest). Several publications relate 200 vegetation height to z 0 (e.g. (Raupach, 1994;Plate, 1971;Brutsaert, 2013)). Here we used the often used equation from (Brutsaert, 2013): The vegetation height is the most important parameter influencing turbulence near the surface, and for this reason the used parameterisation gives a reasonable estimate of z 0 , even though it ignores many other aspects that influence 205 z 0 (e.g. density, vertical distribution of foliage). Multiple studies have illustrated that there is a seasonal variation in z 0 /h for different types of forests (e.g. (Yang and Friedl, 2003;Nakai, 2008)). The z 0 of deciduous trees is therefore additionally linked to the leaf area index to account for changes in tree foliage. The following formula is used to compute the monthly z 0 value for each deciduous forest pixel:

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Here the maximum roughness length z , is set to the LiDAR-derived z 0 from (eq. 4). The minimum roughness length z , represents the z 0 of leafless deciduous trees. Following the dependence of z 0 /h on LAI presented in Nakai (2008) and Yang and Friedl (2003), we set the z , to 80% of z , . Table 1 gives an overview of several studies that relate the z 0 value to the NDVI. The functions are derived for 215 different vegetation types under specific conditions. Equations 6 to 12 are derived for different types of agricultural land. The equations are all within a reasonable range from one another for NDVI values lower than ~0.8. Therefore, we chose to use the average function of eq. 6 to eq. 11 to compute z 0 values for all CLC subcategories of the following DEPAC classes: "arable", "other" and "permanent crops". Figure 2 is a visualization of eq. 6 to 11 and the used mean function. The z 0 value of grasslands is in general lower than other vegetation types. The last equation, eq. 220 12, is specifically derived for grassland and is therefore used for all CLC subcategories that fall under the DEPAC class "grass".

z 0 values for urban areas
The default z 0 of urban areas in LOTOS-EUROS was set to 2 meters. We have updated the z 0 values for urban areas inhabitants/km 2 and to 1 metre in areas with a population density lower than 5000 inhabitants/km 2 . The z 0 values of the other urban subcategories, CLC2012 class 3 to 9, are updated to the proposed values for CLC classes in (Silva et al., 2007).

LAI and z 0 in LOTOS-EUROS
After the computation of the z 0 values, the maps for each CORINE land cover class were merged and converted into DEPAC classes using a pre-defined conversion table. As multiple CORINE land cover may translate to one single DEPAC class, the weighted average based on the respective percentage of each CORINE land cover class was computed for each pixel. We then used linear interpolation to obtain continuous z 0 maps for each DEPAC class.

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Finally, the maps were re-gridded unto the CORINE/Smiatek grid and saved into one file per month.
The default parameterization of the LAI in LOTOS-EUROS is replaced by the MCD15A2H LAI product from MODIS. First, we applied a coordinate transformation to obtain the data in geographical coordinates. The data was then re-gridded unto the grid of the CORINE/Smiatek land use map using linear interpolation. The quality tags were evaluated to identify pixels with default fill values from the MCD15A2H product. These fill-values were replaced   Figure 7. The default LAI of "grass" and "deciduous forest" seems to fit the yearly variation of the 265 MODIS LAI quite well.

Implications for modelled N r deposition fields
In The fourth run, named "LE z0+LAI ", uses both updated LAI and z 0 values. From now on, we will refer to the outputs of these different runs using the abovementioned abbreviations.

Effect on total N r deposition
275 Figure 8 shows the division of the total terrestrial N r deposition over Germany, the Netherlands and Belgium into different N r compounds for each of the model runs. A relatively larger portion of the total N r deposition is attributed to oxidized forms of N r in Germany. The reduced forms of N r predominate in the Netherlands and Belgium. The largest change in total N r deposition occurs in Belgium (+6.19%) due to the inclusion of the MODIS LAI. This corresponds to the relative increase in LAI values here. The inclusion of the updated z 0 values lead to a minor 280 decrease in total N r deposition in the Netherlands (-1.45%) and Belgium (-1.13%), and a minor increase in Germany (+0.44%).

Effect on wet and dry N r deposition
We examined the direct effect of the updated LAI and z 0 values on the modelled dry N r deposition, as well as the related indirect effect in modelled wet N r deposition. Figure 9 shows the dry and wet N r deposition in kg N ha -1 in 285 2014, modelled with the updated LAI and z 0 values as input in LOTOS-EUROS. Figure 10 shows the relative changes in the total amount of dry and wet N r deposition of the different runs with respect to the default run. The combined effect shows an increase of the amount of dry N r deposition over most parts of Belgium and Germany.
The amount of wet N r deposition decreases over most parts of Germany and eastern Belgium, but remains unchanged in north-western parts of Germany. We observe a decrease in total N r deposition in the Netherlands. In 290 general, we observe changes ranging from approximately -20% to +30% in the total amount of dry N r deposition.
The changes in wet N r deposition are smaller in magnitude, and range from approximately -3% to +3%.

Effect on reduced and oxidized N r deposition
We split up the total Nr deposition into NH x (NH 3 and NH 4 + ) and NO y (NO and NO 2 and NO 3 and HNO 3 ) deposition, to look at the effect of the updated LAI and z 0 input maps on the deposition of reduced and oxidised 295 forms of N r , respectively. Figure 11 shows the modelled NH x and NO y deposition in kg N ha -1 in 2014, including the updated LAI and z 0 input values. Figure 12 shows the relative changes (%) in the total NH x and NO y of the different runs with respect to the default run of LOTOS-EUROS. The updated z 0 values have a larger impact on the NH x deposition than on the NO y deposition. The implementation of the updated z 0 values has led to a decrease in NH x deposition over most of the Netherlands, and western Belgium, driven by the large fraction of grassland here. The

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updated LAI values led to relatively more NH x deposition in Belgium. The updated LAI values led to an increase of NO y deposition in almost all areas within the modelled region, except for some urban areas. Moreover, the impact seems to be limited in large forests located in background areas. Table 4 gives an overview of changes in the distribution of the land use specific fluxes in the whole study area 305 (Germany, the Netherlands and Belgium combined) for the different runs. The most distinct changes in N r deposition are due to the updated LAI values ("LE LAI "), where we observe an increase in total N r deposition on urban areas (+ 16.62%) and arable land (+ 9.53%), and a decrease on coniferous forests (-9.36%). This coincides with the categories where we observe the largest changes in LAI values. The default LAI values in urban areas were first set to zero for all urban DEPAC categories. The MODIS LAI values, however, are only zero in densely 310 populated areas and areas with industry. The main effects of the updated z 0 values ("LE z0 ") can be observed for grass (-3.95 %), permanent crops (+ 3.27) and arable land (-3.17 %). In the combined run, "LE z0+LAI ", we observe an amplified effect in total N r deposition over grass (-8.05%) and arable land (+ 12.88%). The impact of the individual parameters on the remaining land use categories are attenuated in this run.

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The changes in N r deposition amounts induce an effect in the modelled distribution of nitrogen components. Here, we look at the effect of the updated LAI and z 0 values on NH 3 and NO 2 surface concentrations. Figure 13 shows the updated modelled NH 3 and NO 2 surface concentrations in 2014. The dots on top of the figures represent the stations that are used for validation, and their observed mean NH 3 and NO 2 surface concentrations. Figure 14 shows the relative change in yearly mean NH 3 and NO 2 surface concentrations in 2014 of the different runs with respect to the 320 default run of LOTOS-EUROS.
The first column represents the changes in NH 3 and NO 2 surface concentrations due to the updated z 0 values. The

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To analyse the effect of the updated LAI and z 0 values, we compared our model output to the available in-situ observations. Due to the lack of available dry deposition measurements, we decided to use NH 4 + and NO 3 wet deposition and NH 3 and NO 2 surface concentrations measurements instead. The distribution of the wet deposition stations is shown in Figure 15, as well as the modelled mean NH 4 + (left) and NO 3 -(right) wet deposition in 2014.
The locations of the stations that measure the NH 3 and NO 2 surface concentrations are shown in Figure 13.

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The relationships between the modelled and observed fields are evaluated using the Pearson's correlation coefficient (r), the root-mean-square error (RMSE) and the coefficients (slope, intercept) of simple linear regression. Table 6 shows these measures for the comparison with monthly mean NO 3 wet deposition, NH 4 + wet deposition, and the Both Table 5 and Figure 17 illustrate that the comparability of the modelled wet deposition and surface concentration fields to the available in-situ measurements did not change significantly. The impact of the updated LAI and z 0 values on these fields is largely an indirect effect of the more distinct changes in the dry deposition, and thus too small to lead to any drastic changes. We conclude that we are unable to demonstrate any major 360 improvements with the use of the currently available in-situ measurements.

Discussion
This paper aimed to improve the surface characterization of LOTOS-EUROS through the inclusion of satellitederived leaf area index (LAI) and roughness length (z 0 ) values. We used empirical functions to derive roughness length (z 0 ) values for different land use classes. The updated z 0 values are compared to z 0 values from several studies 365 (Wieringa, 1993;Silva et al., 2007;Troen and Petersen, 1989;Lankreijer et al., 1993;Yang and Friedl, 2003), and z 0 values used in other CTMs (Simpson et al., 2012;Bessagnet et al., 2017). Table 6 gives an overview of these z 0 values. There is in general good agreement with these z 0 values, and the updated z 0 values mostly fall within the stated ranges. The updated z 0 values for coniferous and deciduous forest are on the high side compared to these studies. These differences can in part be explained by the occurrence of relatively tall forest canopy (~30 meters) in 370 the dataset, especially in forest in southern Germany, whereas most of these studies either assumed or studied shorter trees. Another possible explanation lies in the fact that we used a relatively large conversion factor of 0.136 (eq. 4), whereas a factor of 0.10 is also used quite often.
The updated z 0 values are linked to specific land use pixels and are therefore assumed reasonable estimates for moderately homogeneous areas with this specific land use type. There are various approaches to combine these z 0 375 values into an 'effective' roughness for larger, mixed areas (e.g. (Claussen, 1990;Mason, 1988)). The LOTOS-EUROS model uses logarithmic averaging to compute an effective roughness for an entire model pixel. This averaging step seems to be one of the reasons why the effect of our updated z 0 values on the deposition fields is limited. To illustrate this, the relative change in total dry NH 3 deposition due to the updated z 0 values were computed and shown in Figure 17. We used increasing threshold percentages to sort the NH 3 deposition on a model 380 pixel level per land use type and fraction. Figure 17 shows that the differences in total NH 3 deposition between the two runs increase with increasing land use fraction. The model pixels that mostly consist of one type of land use seem to show the largest change in NH 3 deposition. The change thus appears to be less distinct in pixels that have a higher degree of mixing. Most of the model pixels largely contain mixtures of different land uses on the current model resolution. As a results, averaging of z 0 on a model pixel level is thus likely to cause a levelling effect on the 385 current model resolution. The impact of the updated z 0 values is therefore expected to be larger at a higher model resolution. The use of another approach for computing the 'effective' roughness could potentially lead to stronger changes in the modelled deposition fields. Here, however, we merely focus on updating the z 0 values per land use, and we consider the effect of this beyond the scope of this paper.
Moreover, we should also consider the limitations of the datasets used in this study. The previous versions of the 390 MODIS-LAI have been validated in many studies (e.g. (Fang et al., 2012;Wang et al., 2004;Kobayashi et al., 2010)), showing an overall good agreement with ground observed LAI values and other LAI products. The seasonality in LAI is properly captured for most biomes, but unrealistic temporal variability is observed for forest due to infrequent observations. Also, the previous versions overestimate LAI for forests (Fang et al., 2012;Kobayashi et al., 2010;Wang et al., 2004). Although the MODIS-LAI products have been gradually improving with each update, with the updated z 0 map, and from -20% even up to +30% with the MODIS LAI values. As a result, we observed a shift from wet to dry deposition, except for the Netherlands, where we observe an opposite shift, from dry to wet deposition. Moreover, we observed a redistribution of N r deposition over different land use classes on a sub grid 420 level. To illustrate the potential consequences on a local scale, we computed the critical load exceedances for deciduous and coniferous forest ( Figure 17) using critical loads of 10 kg following Bobbink et al. (2010b).
Compared to the default run, the changes may be sizable locally, ranging from approximately -3 kg up to +2 kg for deciduous forest and even over -3 kg for coniferous forest.
We showed that the corresponding modelled NH 3 surface concentrations may vary up to ~10% locally due to 425 updated z 0 and LAI values, and the NO 2 surface concentrations up to approximately -5%. We compared the outputs from the different runs to available in-situ observations. The changes in modelled NH 3 and NO 2 surface concentration and NH 4 + and NO 3 wet deposition due to the inclusion of the updated z 0 and LAI values were, however, relatively small. As a result, we were not able to identify significant improvements in the comparability with in-situ measurements.

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This work has shown that changes in two of the main deposition parameters (LAI, z 0 ) can already lead to distinct changes (~30%) in the modelled deposition fields. This demonstrates the model's sensitivity toward these input The surface-atmosphere exchange remains one of the most important uncertainties in deposition modelling. The use of satellite products to derive LAI and z 0 values is, in our view, an initial step toward a more accurate representation of the surface characterization in models, that might help us to minimize the uncertainty in deposition modelling.

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The approach to derive high resolution, dynamic z 0 estimates presented here can be linked to any land use map and is as such transferable to many different models and geographical areas.

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The third row shows the combined effect of both these updates.   Table 5: Correlation coefficient r, root-mean-square difference, slope and intercept of the different in-situ networks in 695 comparison with the corresponding values from the different model runs.