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In regions with a Nordic climate, soil erosion rates in winter and early spring can exceed those occurring during other seasons of the year. In this context, this study was initiated to improve our understanding of the interaction between agricultural soils and occurring winter conditions. The main objective was to better understand how hydrological processes in a catchment are influenced by snow, ice, and freeze-thaw cycles of soils, leading to runoff and soil erosion in winter and spring conditions. For this purpose, detailed spatially and temporally distributed measurements and observations in a small catchment in Norway were executed during three consecutive winter/spring periods. During the winter/spring periods of 2013-2014, 2014-2015 and 2015-2016, soil water content, soil temperature, and snow cover properties were measured. In addition, numerous soil samples were taken to determine the soil hydraulic characteristics of the investigated soils and to quantify the changes in their macropore networks due to freeze-thaw events, using X-ray imaging. With the collected data and deduced process understanding, it was possible to model and quantify the spatial and temporal development of snow packs. Furthermore, the field observations revealed how the interaction of tillage, state of the soils and snow cover at a certain time can lead to none or extensive surface runoff and soil erosion. Integrating acquired data, observations and process knowledge facilitated advances in simulating and quantifying surface runoff and soil erosion rates across the catchment under investigation. The models applied and the maps and output derived are crucial elements for presenting current state and problems in the catchment to stakeholders (such as farmers), providing a starting point for discussing ways to prevent and reduce further runoff and erosion. For model calibration and validation, including interpretation of modelling results, good knowledge of the area and availability of detailed data are essential, especially when processes such as freezing-thawing of soils and ice layer and snow-pack dynamics have to be considered also. In order to reduce runoff and soil erosion during winter and snowmelt conditions in the future, more targeted research is required in order to address the full range of existing knowledge gaps in this field, as identified in this particular study also.

The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico in 2010 was one of the largest oil spills in history. For three months, oil leaked from the Macondo well at 1,500 m depth into the Gulf. As one of the spill responses, an unprecedented amount of dispersants were applied, both at the sea surface and, for the first time ever, directly injected into the wellhead. During the spill, unusually large amounts of marine snow, including Extracellular Polymeric Substances (EPS), were formed. Oil-contaminated marine snow aggregates were formed by aggregation of EPS with suspended solids, phytoplankton cells due to the spring bloom, and the dispersed oil droplets. The marine snow sank through the water column and settled on the ocean floor. This process was named MOSSFA: Marine Oil Snow Sedimentation and Flocculent Accumulation. MOSSFA was an important pathway of transferring oil to the deep-sea, and 14-21% of the total discharged oil is estimated to have settled on the sediment, where it impacted the benthic ecosystem. This thesis focused first on the mechanism of EPS snow formation, and then more in depth on the additional ecotoxicological consequences of marine snow formation during oil spills for the benthic ecosystem. Chapter 2 describes the role of chemical dispersants in the presence of phytoplankton in the formation of EPS, one of the main ingredients of marine snow. Results show that phytoplankton-associated bacteria were responsible for the EPS formation, and the symbiosis between the phytoplankton and its associated bacterial community provided the bacteria with energy to produce the EPS. The microcosm experiment in Chapter 3 investigated the effect of marine snow on oil biodegradation in microcosms without benthic macroinvertebrates. Results showed that marine snow hampers oil biodegradation: the presence of marine snow reduced the depletion of oil alkanes by 40%, most likely due to the high biodegradability of marine snow organics compared to the oil. Biodegradation of marine snow resulted in anaerobic conditions in the top of the sediment layer. This reduced the oil biodegradation. Marine snow thus prolongs the residence time of oil in the benthic ecosystem. The next microcosm experiment, described in Chapter 4, investigated the effects of oil-contaminated marine snow on benthic macroinvertebrates, and the effect of macroinvertebrates on oil biodegradation. Bioturbation by the invertebrates increased the oxygenated top layer of the sediment and partly counterbalanced the inhibition of oil biodegradation due to oxygen consumption by marine snow. Survival of three benthic invertebrate species was reduced by (oil-contaminated) marine snow. Oxygen depletion near the sediment surface seemed to be the main reason for the observed adverse effects of the marine snow. In addition, indications were found that some species used the marine snow as food source, even when it was oil-contaminated. In the last microcosm experiment, described in Chapter 5, two benthic invertebrate species were monitored over a period of 42 days after which new animals were introduced and observed for an additional period of 22 days. Marine snow degradation again resulted in lower dissolved oxygen concentrations in the water column, which inhibited oil biodegradation on the sediment compared to oil in combination with clay. The oxygenated top layer of the sediment disappeared, and recovered after ~20 days. At the end of the experiment, mudsnails from the treatments with oiled marine snow had higher PAH concentrations in their tissues than the animals from the treatments with the same amount of oil in clay only, confirming the use of marine snow as food source. Overall, oil-contaminated marine snow on the ocean sediment can negatively affect benthic ecosystems, and can hamper oil biodegradation and ecosystem recovery. The additional consequences of MOSSFA during oil spills and spill responses should be taken into account in oil spill response planning.

Thesis entitled: Modelling Stable Atmospheric Boundary Layers over Snow H.A.M. Sterk Wageningen, 29th of April, 2015 Summary The emphasis of this thesis is on the understanding and forecasting of the Stable Boundary Layer (SBL) over snow-covered surfaces. SBLs typically form at night and in polar regions (especially in winter), when radiative cooling at the surface causes a cooler surface than the overlying atmosphere and a stable stratification develops. This means that potential temperature increases with height and buoyancy effects suppress turbulence. Turbulence is then dominated by mechanical origin. If sufficient wind shear can be maintained, turbulence remains active, otherwise it will cease. A proper representation of SBLs in numerical weather prediction models is critical, since many parties rely on these forecasts. For example, weather prediction is needed for wind energy resources, agricultural purposes, air-quality studies, and aviation and road traffic. Knowledge on SBLs is also essential for climate modelling. In the Arctic regions, climate change is most pronounced due to stronger changes in near-surface temperature compared to other latitudes. Though this `Arctic amplification' is not yet fully understood, possible responsible processes are the ice-albedo feedback, alterations in cloud cover and water vapour, different atmospheric and oceanic circulations, and the weak vertical mixing in the lower atmosphere. However, many interactions exist between these processes. With positive feedbacks, changes are even further enhanced. This could have worldwide consequences, i.e. due to affected atmospheric circulations and sea level rise with Greenland's melting ice-sheets. Scientists try to explain the observed climate changes, as well as provide outlooks for future changes in climate and weather. However, the understanding is hampered by the fact that many model output variables (e.g. regarding the 2 m temperature) vary substantially between models on the one hand, and from observations on the other hand. Modelling the SBL remains difficult, because the physical processes at hand are represented in a simplified way, and the understanding of the processes may be incomplete. Furthermore, since processes can play a role at a very small scale, the resolutions in models may be too poor to represent the SBL correctly. Additionally, there are many different archetypes of the SBL. Turbulence can be continuous, practically absent, or intermittent, and vary in strength which affects the efficiency of the exchange of quantities horizontally and vertically. Processes that are considered critical for the SBL evolution are e.g. turbulent mixing, radiative effects, the coupling between the atmosphere and the underlying surface, the presence of clouds or fog, subsidence, advection, gravity waves, and drainage and katabatic flows. In this thesis, the focus is on the first three processes, as these are most dominant for the evolution of the SBL (e.g. Bosveld et al., 2014b). In Chapter 3 an idealized clear-sky case over sea-ice was studied based on the GABLS1 benchmark study (e.g. Cuxart et al., 2006), but extended by including radiative effects and thermal coupling with the surface. Hence the following research questions were posed: Question 1: What is the variety in model outcome regarding potential temperature and wind speed profiles that can be simulated with one model by using different parametrization schemes? Question 2: Which of the three governing processes is most critical in determining the SBL state in various wind regimes? Question 3: Can we identify compensation mechanisms between schemes, and thus identify where possible compensating model errors may be concealed? From the analysis with different parametrization schemes performed with the WRF single-column model (SCM, Question 1), it followed that quite different types of SBLs were found. Some schemes forecasted a somewhat better mixed potential temperature profile where stratification increased with height, while another scheme produced profiles with the strongest stratification close to the surface and stratification decreased with height. After only 9 h of simulation time, a difference in temperature of almost 2 K was found near the surface. Regarding the wind speed profile, some variation was found in the simulated low-level jet speed and height. Mainly the difference in atmospheric boundary-layer (BL) schemes which parametrize the turbulent mixing are responsible for these model output variations. A variation in long wave parametrization schemes hardly affected the model results. Question 2 addresses the problem whether other processes than turbulent mixing may be responsible for a similar spread in model results. A sensitivity analysis was performed where for one set of reference parametrization schemes the intensity of the processes was adjusted. The relative sensitivity of the three processes for different wind regimes was analysed using `process diagrams'. In a process diagram, two physically related variables are plotted against each other, which in this case represent either a time average or a difference over time of the variable. A line connects the reference state with the state for which the process intensity is modified. By comparing the length and orientation of the lines, the relative significance of the individual processes for the different wind speeds can be studied. Overlapping line directions identify possible compensating errors. Geostrophic wind speeds of 3, 8 and 20 m s-1 were selected representing low, medium and high wind speeds, capturing the range of wind speeds frequently occurring in the Arctic north of 75oN according to the ERA-Interim reanalysis dataset. Overall, a shift in relative importance was detected for the various wind regimes. With high geostrophic wind speeds, the model output is most sensitive to turbulent mixing. On the contrary, with low geostrophic wind speeds the model is most sensitive to the radiation and especially the snow-surface coupling. The impact of turbulent mixing is then minor, unless when mixing in both boundary layer and surface layer is adjusted. This stresses that proper linking between these two layers is essential. Also with one set of parametrization schemes different SBL types were simulated. Potential temperature profiles were better mixed (increasing stratification with height) for high geostrophic wind speeds, and this tended to develop to profiles with the strongest stratification near the surface (decreasing stratification with height) for low geostrophic wind speeds. However, a variety in types was also found when keeping the same wind regime, but by varying the mixing strength. With enhanced mixing, the profile became better mixed, also when the reference profile showed the strongest stratification near the surface. With decreased mixing, profiles with a stronger stratification were found, again shaped with the strongest stratification near the surface. Thus a different mixing formulation has a strong impact on the vertical profiles, even when it may not necessarily strongly affect the surface variables. Therefore, it is recommended that when a model is evaluated and optimized, the vertical structure is also regarded in this process, since near-surface variables may be well represented, strong deviations aloft are still possible. Furthermore, the process diagrams showed overlap in sensitivity to some processes. Therefore errors within the parametrizations of these processes could compensate each other and thus remain hidden (Question 3), making the model formulation possibly physically less realistic. This study did not reveal an unambiguous indication for the compensating processes regarding the various sets of variables, though overlap for single variables is seen. This study also revealed a non-linear behaviour regarding the 2 m temperature, which is also found in observations (e.g. Lüpkes et al., 2008) and in a model study by McNider et al. (2012). Here the 2 m temperature decreased with enhanced mixing strength and increased with a lower mixing intensity. This counter-intuitive behaviour is explained by that mixing only occurs in a shallow layer close to the surface. Cold air that is mixed up by the enhanced mixing, is insufficiently compensated by the downward mixed relatively warm air. This behaviour was found mostly for low wind speeds or with decreased mixing at the medium wind regime, when the potential temperature profile showed the strongest stratification near the surface. The study proceeds with a model evaluation against observations in low wind speed regimes. Three stably stratified cloud-free study cases with near-surface wind speeds below 5 m s-1 were selected with each a different surface: Cabauw in the Netherlands with snow over grass, Sodankylä in northern Finland with snow in a needle-leaf forest, and Halley in Antarctica with snow on an ice shelf. Chapter 4 presents the evaluation of the WRF-3D and SCM for these cases. In this study, the WRF-3D model was used to determine the forcings, as often not all the required observations at high resolution in time and space are available. Hence the following questions were formulated: Question 4: What is the performance of WRF in stable conditions with low wind speeds for three contrasting snow-covered sites? Question 5: How should we prescribe the single-column model forcings, using WRF-3D? The standard WRF-3D simulation had an incorrect representation of the snow-cover and vegetation fraction, which deteriorates the conductive heat flux, the surface temperatures and the SBL evolution. Indeed, Chapter 3 highlighted the critical role of the land-surface coupling representation. Adjusting the settings with site specific information, improved model simulations compared with the observations. In general, the performance of WRF-3D was quite good for the selected cases, especially regarding the wind speed simulations. The temperature forecast proved to be more challenging. For Cabauw and Sodankylä, 2 m temperatures were strongly overestimated, though a better simulation was seen at higher tower levels. For Halley a better representation of the 2 m temperature was found, though aloft potential temperatures were underestimated. Hence, the three cases had an underestimated modelled temperature gradient in common. This study also investigated how the forcing fields for the SCM should be prescribed. Model results for the three study cases all showed a significant deviation from the observed wind field without lateral forcings and time-invariant geostrophic wind speed. Including only a time-varying geostrophic wind speed did not improve the results. Prescribing additional momentum advection did have a positive impact on the modelled wind speed. The results regarding temperature, specific humidity and their stratification improved when temperature and humidity advection was also taken into account. Forcing the SCM field towards a prescribed 3D atmospheric state is not recommended, since unrealistic profiles were found below the threshold forcing height. Having established the optimal model set-up, the SCM can be used as a tool to further study the small-scale processes for the three study cases, addressing the following questions: Question 6: How do the model results with various process intensities compare with observations? Question 7: Are any differences in relative process impacts found for the three contrasting sites? Question 8: Does the model sensitivity vary between two different BL schemes? The sensitivity analysis was performed with the WRF-SCM and repeated for two BL schemes. In general, the temperature and humidity stratifications intensified by decreasing the process strengths and hence were in better agreement with observations than the reference cases. The wind field was most sensitive to turbulent mixing, with a weaker low-level jet at a higher altitude for enhanced mixing and the opposite for less mixing, while the impact of the other processes was small. Contrary to the temperature profiles, a better agreement with wind observations was found with amplified mixing, except for Halley where results improved with reduced mixing. Regarding the surface energy budget, the conductive heat flux was greatly overestimated at Cabauw due to an overestimated snow conductivity, while better agreements were found for the other sites. A revision of the definition for snow conductivity in the model is recommended, because rather large values were assumed for fresh snow, and indeed results improved when the coupling strength was reduced for Cabauw and Sodankylä. For Halley almost the same snow conductivity was modelled as was used to determine the observed conductive heat flux, however, then the temperature gradient through the first soil/snow layer was underestimated leaving the flux too small. The net radiation was strongly too negative for the Cabauw and Halley case-studies. This is likely due to an underestimation of the incoming long wave radiation as part of a deficiency in the long wave radiation scheme. For all sites the sensible heat flux was overestimated, and decreased mixing improved the results. However, the eddy covariance measurements may have been made outside the constant flux layer, which hampers the model evaluation. Though Question 6 aims to obtain understanding in which processes are most responsible for simulating model results that are in closer agreement with observations, measuring in these cold and dry circumstances is especially challenging. Furthermore, the measurements are mostly point measurements while the model grid represents a larger area, such that the measurements may be influenced by local features which are not captured in the model. These issues hinders a clear comparison of the model results with observations, and the observation uncertainties may be greater than what was represented in the process diagrams. When comparing the process sensitivity for the different sites (Question 7), we found some distinct variations in relative process significance. The radiation impact was relatively large at Cabauw and Sodankylä where the specific humidity was higher such that a larger impact on the incoming long wave radiation can be obtained. The snow-surface coupling is more important at Halley. This is related to the higher snow cover at Halley compared to the other sites. Additionally, the conductivity of the underlying medium at Halley is set equal to that of snow. These two factors ensure that the impact of an altered snow conductivity is greater. From the comparison of the sensitivity analyses for the two BL schemes (MYJ and YSU, Question 8), it followed that the overall direction of the sensitivity orientation is similar. However, stronger BL temperature stratifications were found with YSU, though between the surface and the first model level stronger stratifications were simulated with MYJ. This is related to the relatively high ratio of mixing in the boundary layer versus the surface layer with MYJ. Therefore the mixing in the BL is relatively more efficient and the surface layer cannot keep up the mixing to keep a smooth profile at the surface-layer / boundary-layer interface. This indicates the importance of a consistent transition between the BL and surface layer, as also pointed out by Svensson and Holtslag (2009). Furthermore, the non-linearity concerning the 2 m temperature behaviour discussed earlier is most profound with YSU, and not as obvious with MYJ due to a stronger implemented minimum diffusivity. The results point towards the direction of focus for future research. This could be achieved by e.g. re-evaluating the snow representation, as well as investigating the long-standing problem of the underestimated long wave radiation. Additionally, the mixing seems to be too high in some of the simulations. As such, care should be taken in choosing the BL scheme and its constraints on the mixing, as these may hamper the development of the observed behaviour on non-linear near-surface temperature evolution for example.

Thesis entitled: Modelling Stable Atmospheric Boundary Layers over Snow H.A.M. Sterk Wageningen, 29th of April, 2015 Summary The emphasis of this thesis is on the understanding and forecasting of the Stable Boundary Layer (SBL) over snow-covered surfaces. SBLs typically form at night and in polar regions (especially in winter), when radiative cooling at the surface causes a cooler surface than the overlying atmosphere and a stable stratification develops. This means that potential temperature increases with height and buoyancy effects suppress turbulence. Turbulence is then dominated by mechanical origin. If sufficient wind shear can be maintained, turbulence remains active, otherwise it will cease. A proper representation of SBLs in numerical weather prediction models is critical, since many parties rely on these forecasts. For example, weather prediction is needed for wind energy resources, agricultural purposes, air-quality studies, and aviation and road traffic. Knowledge on SBLs is also essential for climate modelling. In the Arctic regions, climate change is most pronounced due to stronger changes in near-surface temperature compared to other latitudes. Though this `Arctic amplification' is not yet fully understood, possible responsible processes are the ice-albedo feedback, alterations in cloud cover and water vapour, different atmospheric and oceanic circulations, and the weak vertical mixing in the lower atmosphere. However, many interactions exist between these processes. With positive feedbacks, changes are even further enhanced. This could have worldwide consequences, i.e. due to affected atmospheric circulations and sea level rise with Greenland's melting ice-sheets. Scientists try to explain the observed climate changes, as well as provide outlooks for future changes in climate and weather. However, the understanding is hampered by the fact that many model output variables (e.g. regarding the 2 m temperature) vary substantially between models on the one hand, and from observations on the other hand. Modelling the SBL remains difficult, because the physical processes at hand are represented in a simplified way, and the understanding of the processes may be incomplete. Furthermore, since processes can play a role at a very small scale, the resolutions in models may be too poor to represent the SBL correctly. Additionally, there are many different archetypes of the SBL. Turbulence can be continuous, practically absent, or intermittent, and vary in strength which affects the efficiency of the exchange of quantities horizontally and vertically. Processes that are considered critical for the SBL evolution are e.g. turbulent mixing, radiative effects, the coupling between the atmosphere and the underlying surface, the presence of clouds or fog, subsidence, advection, gravity waves, and drainage and katabatic flows. In this thesis, the focus is on the first three processes, as these are most dominant for the evolution of the SBL (e.g. Bosveld et al., 2014b). In Chapter 3 an idealized clear-sky case over sea-ice was studied based on the GABLS1 benchmark study (e.g. Cuxart et al., 2006), but extended by including radiative effects and thermal coupling with the surface. Hence the following research questions were posed: Question 1: What is the variety in model outcome regarding potential temperature and wind speed profiles that can be simulated with one model by using different parametrization schemes? Question 2: Which of the three governing processes is most critical in determining the SBL state in various wind regimes? Question 3: Can we identify compensation mechanisms between schemes, and thus identify where possible compensating model errors may be concealed? From the analysis with different parametrization schemes performed with the WRF single-column model (SCM, Question 1), it followed that quite different types of SBLs were found. Some schemes forecasted a somewhat better mixed potential temperature profile where stratification increased with height, while another scheme produced profiles with the strongest stratification close to the surface and stratification decreased with height. After only 9 h of simulation time, a difference in temperature of almost 2 K was found near the surface. Regarding the wind speed profile, some variation was found in the simulated low-level jet speed and height. Mainly the difference in atmospheric boundary-layer (BL) schemes which parametrize the turbulent mixing are responsible for these model output variations. A variation in long wave parametrization schemes hardly affected the model results. Question 2 addresses the problem whether other processes than turbulent mixing may be responsible for a similar spread in model results. A sensitivity analysis was performed where for one set of reference parametrization schemes the intensity of the processes was adjusted. The relative sensitivity of the three processes for different wind regimes was analysed using `process diagrams'. In a process diagram, two physically related variables are plotted against each other, which in this case represent either a time average or a difference over time of the variable. A line connects the reference state with the state for which the process intensity is modified. By comparing the length and orientation of the lines, the relative significance of the individual processes for the different wind speeds can be studied. Overlapping line directions identify possible compensating errors. Geostrophic wind speeds of 3, 8 and 20 m s-1 were selected representing low, medium and high wind speeds, capturing the range of wind speeds frequently occurring in the Arctic north of 75oN according to the ERA-Interim reanalysis dataset. Overall, a shift in relative importance was detected for the various wind regimes. With high geostrophic wind speeds, the model output is most sensitive to turbulent mixing. On the contrary, with low geostrophic wind speeds the model is most sensitive to the radiation and especially the snow-surface coupling. The impact of turbulent mixing is then minor, unless when mixing in both boundary layer and surface layer is adjusted. This stresses that proper linking between these two layers is essential. Also with one set of parametrization schemes different SBL types were simulated. Potential temperature profiles were better mixed (increasing stratification with height) for high geostrophic wind speeds, and this tended to develop to profiles with the strongest stratification near the surface (decreasing stratification with height) for low geostrophic wind speeds. However, a variety in types was also found when keeping the same wind regime, but by varying the mixing strength. With enhanced mixing, the profile became better mixed, also when the reference profile showed the strongest stratification near the surface. With decreased mixing, profiles with a stronger stratification were found, again shaped with the strongest stratification near the surface. Thus a different mixing formulation has a strong impact on the vertical profiles, even when it may not necessarily strongly affect the surface variables. Therefore, it is recommended that when a model is evaluated and optimized, the vertical structure is also regarded in this process, since near-surface variables may be well represented, strong deviations aloft are still possible. Furthermore, the process diagrams showed overlap in sensitivity to some processes. Therefore errors within the parametrizations of these processes could compensate each other and thus remain hidden (Question 3), making the model formulation possibly physically less realistic. This study did not reveal an unambiguous indication for the compensating processes regarding the various sets of variables, though overlap for single variables is seen. This study also revealed a non-linear behaviour regarding the 2 m temperature, which is also found in observations (e.g. Lüpkes et al., 2008) and in a model study by McNider et al. (2012). Here the 2 m temperature decreased with enhanced mixing strength and increased with a lower mixing intensity. This counter-intuitive behaviour is explained by that mixing only occurs in a shallow layer close to the surface. Cold air that is mixed up by the enhanced mixing, is insufficiently compensated by the downward mixed relatively warm air. This behaviour was found mostly for low wind speeds or with decreased mixing at the medium wind regime, when the potential temperature profile showed the strongest stratification near the surface. The study proceeds with a model evaluation against observations in low wind speed regimes. Three stably stratified cloud-free study cases with near-surface wind speeds below 5 m s-1 were selected with each a different surface: Cabauw in the Netherlands with snow over grass, Sodankylä in northern Finland with snow in a needle-leaf forest, and Halley in Antarctica with snow on an ice shelf. Chapter 4 presents the evaluation of the WRF-3D and SCM for these cases. In this study, the WRF-3D model was used to determine the forcings, as often not all the required observations at high resolution in time and space are available. Hence the following questions were formulated: Question 4: What is the performance of WRF in stable conditions with low wind speeds for three contrasting snow-covered sites? Question 5: How should we prescribe the single-column model forcings, using WRF-3D? The standard WRF-3D simulation had an incorrect representation of the snow-cover and vegetation fraction, which deteriorates the conductive heat flux, the surface temperatures and the SBL evolution. Indeed, Chapter 3 highlighted the critical role of the land-surface coupling representation. Adjusting the settings with site specific information, improved model simulations compared with the observations. In general, the performance of WRF-3D was quite good for the selected cases, especially regarding the wind speed simulations. The temperature forecast proved to be more challenging. For Cabauw and Sodankylä, 2 m temperatures were strongly overestimated, though a better simulation was seen at higher tower levels. For Halley a better representation of the 2 m temperature was found, though aloft potential temperatures were underestimated. Hence, the three cases had an underestimated modelled temperature gradient in common. This study also investigated how the forcing fields for the SCM should be prescribed. Model results for the three study cases all showed a significant deviation from the observed wind field without lateral forcings and time-invariant geostrophic wind speed. Including only a time-varying geostrophic wind speed did not improve the results. Prescribing additional momentum advection did have a positive impact on the modelled wind speed. The results regarding temperature, specific humidity and their stratification improved when temperature and humidity advection was also taken into account. Forcing the SCM field towards a prescribed 3D atmospheric state is not recommended, since unrealistic profiles were found below the threshold forcing height. Having established the optimal model set-up, the SCM can be used as a tool to further study the small-scale processes for the three study cases, addressing the following questions: Question 6: How do the model results with various process intensities compare with observations? Question 7: Are any differences in relative process impacts found for the three contrasting sites? Question 8: Does the model sensitivity vary between two different BL schemes? The sensitivity analysis was performed with the WRF-SCM and repeated for two BL schemes. In general, the temperature and humidity stratifications intensified by decreasing the process strengths and hence were in better agreement with observations than the reference cases. The wind field was most sensitive to turbulent mixing, with a weaker low-level jet at a higher altitude for enhanced mixing and the opposite for less mixing, while the impact of the other processes was small. Contrary to the temperature profiles, a better agreement with wind observations was found with amplified mixing, except for Halley where results improved with reduced mixing. Regarding the surface energy budget, the conductive heat flux was greatly overestimated at Cabauw due to an overestimated snow conductivity, while better agreements were found for the other sites. A revision of the definition for snow conductivity in the model is recommended, because rather large values were assumed for fresh snow, and indeed results improved when the coupling strength was reduced for Cabauw and Sodankylä. For Halley almost the same snow conductivity was modelled as was used to determine the observed conductive heat flux, however, then the temperature gradient through the first soil/snow layer was underestimated leaving the flux too small. The net radiation was strongly too negative for the Cabauw and Halley case-studies. This is likely due to an underestimation of the incoming long wave radiation as part of a deficiency in the long wave radiation scheme. For all sites the sensible heat flux was overestimated, and decreased mixing improved the results. However, the eddy covariance measurements may have been made outside the constant flux layer, which hampers the model evaluation. Though Question 6 aims to obtain understanding in which processes are most responsible for simulating model results that are in closer agreement with observations, measuring in these cold and dry circumstances is especially challenging. Furthermore, the measurements are mostly point measurements while the model grid represents a larger area, such that the measurements may be influenced by local features which are not captured in the model. These issues hinders a clear comparison of the model results with observations, and the observation uncertainties may be greater than what was represented in the process diagrams. When comparing the process sensitivity for the different sites (Question 7), we found some distinct variations in relative process significance. The radiation impact was relatively large at Cabauw and Sodankylä where the specific humidity was higher such that a larger impact on the incoming long wave radiation can be obtained. The snow-surface coupling is more important at Halley. This is related to the higher snow cover at Halley compared to the other sites. Additionally, the conductivity of the underlying medium at Halley is set equal to that of snow. These two factors ensure that the impact of an altered snow conductivity is greater. From the comparison of the sensitivity analyses for the two BL schemes (MYJ and YSU, Question 8), it followed that the overall direction of the sensitivity orientation is similar. However, stronger BL temperature stratifications were found with YSU, though between the surface and the first model level stronger stratifications were simulated with MYJ. This is related to the relatively high ratio of mixing in the boundary layer versus the surface layer with MYJ. Therefore the mixing in the BL is relatively more efficient and the surface layer cannot keep up the mixing to keep a smooth profile at the surface-layer / boundary-layer interface. This indicates the importance of a consistent transition between the BL and surface layer, as also pointed out by Svensson and Holtslag (2009). Furthermore, the non-linearity concerning the 2 m temperature behaviour discussed earlier is most profound with YSU, and not as obvious with MYJ due to a stronger implemented minimum diffusivity. The results point towards the direction of focus for future research. This could be achieved by e.g. re-evaluating the snow representation, as well as investigating the long-standing problem of the underestimated long wave radiation. Additionally, the mixing seems to be too high in some of the simulations. As such, care should be taken in choosing the BL scheme and its constraints on the mixing, as these may hamper the development of the observed behaviour on non-linear near-surface temperature evolution for example.

IJskernen worden geboord in de polaire en alpine gebieden om informatie over het klimaat van het verleden te verkrijgen. Een ijskap bestaat uit de neerslag van het verleden: het oudste ijs is 800.000 jaar oud in Antarctica en 125.000 jaar oud in Groenland. De isotopische samenstelling van de sneeuw van een ijskap is gerelateerd aan de temperatuur in de wolk waarin de sneeuw gevormd is. Door het meten van de isotopische samenstelling van het ijs van een boorkern is het mogelijk een indicatie van de atmosferische temperatuur in het verleden te krijgen. De interpretatie van het isotopensignaal in de ijskern is echter bemoeilijkt doordat het signaal in het ijs niet volledig behouden blijft. Voornamelijk in de periode waarin de sneeuw samengeperst wordt tot ijs vinden meerdere processen plaats die leiden tot een verandering van het signaal. In deze periode wordt de sneeuw firn genoemd. Het belangrijkste proces dat plaatsvindt is diffusie, die veroorzaakt wordt door de willekeurige bewegingen van waterdamp moleculen in de poriën van de sneeuw, gecombineerd met de voortdurende uitwisseling tussen damp- en vaste fase van de watermoleculen. Dit leidt tot een demping van het originele signaal en kan er zelfs voor zorgen dat seizoensvariaties verdwijnen. Dit zorgt er uiteraard voor dat de klimatologische interpretatie van het isotopensignaal in het ijs bemoeilijkt wordt. In dit proefschrift wordt een theoretische beschrijving van het diffusieproces in firn gegeven, wat vervolgens gebruikt wordt om een nieuwe onafhankelijke proxy voor de temperatuur van de sneeuw te verkrijgen. Daarnaast is firndiffusie gemeten in een laboratoriumexperiment en wordt diffusie theorie toegepast in een vergelijking tussen ijskern- en neerslagdata.

IJskernen worden geboord in de polaire en alpine gebieden om informatie over het klimaat van het verleden te verkrijgen. Een ijskap bestaat uit de neerslag van het verleden: het oudste ijs is 800.000 jaar oud in Antarctica en 125.000 jaar oud in Groenland. De isotopische samenstelling van de sneeuw van een ijskap is gerelateerd aan de temperatuur in de wolk waarin de sneeuw gevormd is. Door het meten van de isotopische samenstelling van het ijs van een boorkern is het mogelijk een indicatie van de atmosferische temperatuur in het verleden te krijgen. De interpretatie van het isotopensignaal in de ijskern is echter bemoeilijkt doordat het signaal in het ijs niet volledig behouden blijft. Voornamelijk in de periode waarin de sneeuw samengeperst wordt tot ijs vinden meerdere processen plaats die leiden tot een verandering van het signaal. In deze periode wordt de sneeuw firn genoemd. Het belangrijkste proces dat plaatsvindt is diffusie, die veroorzaakt wordt door de willekeurige bewegingen van waterdamp moleculen in de poriën van de sneeuw, gecombineerd met de voortdurende uitwisseling tussen damp- en vaste fase van de watermoleculen. Dit leidt tot een demping van het originele signaal en kan er zelfs voor zorgen dat seizoensvariaties verdwijnen. Dit zorgt er uiteraard voor dat de klimatologische interpretatie van het isotopensignaal in het ijs bemoeilijkt wordt. In dit proefschrift wordt een theoretische beschrijving van het diffusieproces in firn gegeven, wat vervolgens gebruikt wordt om een nieuwe onafhankelijke proxy voor de temperatuur van de sneeuw te verkrijgen. Daarnaast is firndiffusie gemeten in een laboratoriumexperiment en wordt diffusie theorie toegepast in een vergelijking tussen ijskern- en neerslagdata.

IJskernen worden geboord in de polaire en alpine gebieden om informatie over het klimaat van het verleden te verkrijgen. Een ijskap bestaat uit de neerslag van het verleden: het oudste ijs is 800.000 jaar oud in Antarctica en 125.000 jaar oud in Groenland. De isotopische samenstelling van de sneeuw van een ijskap is gerelateerd aan de temperatuur in de wolk waarin de sneeuw gevormd is. Door het meten van de isotopische samenstelling van het ijs van een boorkern is het mogelijk een indicatie van de atmosferische temperatuur in het verleden te krijgen. De interpretatie van het isotopensignaal in de ijskern is echter bemoeilijkt doordat het signaal in het ijs niet volledig behouden blijft. Voornamelijk in de periode waarin de sneeuw samengeperst wordt tot ijs vinden meerdere processen plaats die leiden tot een verandering van het signaal. In deze periode wordt de sneeuw firn genoemd. Het belangrijkste proces dat plaatsvindt is diffusie, die veroorzaakt wordt door de willekeurige bewegingen van waterdamp moleculen in de poriën van de sneeuw, gecombineerd met de voortdurende uitwisseling tussen damp- en vaste fase van de watermoleculen. Dit leidt tot een demping van het originele signaal en kan er zelfs voor zorgen dat seizoensvariaties verdwijnen. Dit zorgt er uiteraard voor dat de klimatologische interpretatie van het isotopensignaal in het ijs bemoeilijkt wordt. In dit proefschrift wordt een theoretische beschrijving van het diffusieproces in firn gegeven, wat vervolgens gebruikt wordt om een nieuwe onafhankelijke proxy voor de temperatuur van de sneeuw te verkrijgen. Daarnaast is firndiffusie gemeten in een laboratoriumexperiment en wordt diffusie theorie toegepast in een vergelijking tussen ijskern- en neerslagdata.

Dit onderzoek, een coproductie van het LEI en IRMA, beschrijft de mogelijkheden van een brede weersverzekering in de agrarische sector. In een brede weersverzekering dienen zoveel mogelijk weerrisico's te worden meegenomen, zoals storm, hagel, regen, sneeuw, vorst en droogte. Er is enerzijds aandacht voor de theoretische aspecten die het mogelijk maken om al dan niet tot een brede weersverzekering te komen. Anderzijds is er een stakeholderanalyse uitgevoerd onder sectorvertegenwoordigers en verzekeraars. Hierbij draait het om de vraag welke visies, belangen, wensen en ambities een rol spelen bij de opzet van een brede weersverzekering in de praktijk. This study, carried out jointly by LEI and IRMA, describes the options for broad weather insurance within the agricultural sector. As many weather-related risks as possible must be considered within broad weather insurance, such as gales, hail, rain, snow, frost and drought. On the one hand, attention is devoted to the theoretical aspects that may or may not make it possible to put together a broad weather insurance scheme. On the other hand, a stakeholder analysis has been carried out among sector representatives and insurance companies. The core issues here are which visions, interests, wishes and ambitions play a role in setting up a broad weather insurance scheme in practice.

Dit onderzoek, een coproductie van het LEI en IRMA, beschrijft de mogelijkheden van een brede weersverzekering in de agrarische sector. In een brede weersverzekering dienen zoveel mogelijk weerrisico's te worden meegenomen, zoals storm, hagel, regen, sneeuw, vorst en droogte. Er is enerzijds aandacht voor de theoretische aspecten die het mogelijk maken om al dan niet tot een brede weersverzekering te komen. Anderzijds is er een stakeholderanalyse uitgevoerd onder sectorvertegenwoordigers en verzekeraars. Hierbij draait het om de vraag welke visies, belangen, wensen en ambities een rol spelen bij de opzet van een brede weersverzekering in de praktijk. This study, carried out jointly by LEI and IRMA, describes the options for broad weather insurance within the agricultural sector. As many weather-related risks as possible must be considered within broad weather insurance, such as gales, hail, rain, snow, frost and drought. On the one hand, attention is devoted to the theoretical aspects that may or may not make it possible to put together a broad weather insurance scheme. On the other hand, a stakeholder analysis has been carried out among sector representatives and insurance companies. The core issues here are which visions, interests, wishes and ambitions play a role in setting up a broad weather insurance scheme in practice.

The snowmelt runoff model (SRM) uses a degree-day approach for melting snow in a basin. A simple radiation component was combined with the degree-day approach (restricted degree-day method) in an effort to improve estimates of snowmelt and reduce the need to adjust the melt factor over the ablation season. A daily energy balance model was formulated that requires not only the input of radiation but also measurements of daily wind speed, air temperature, and relative humidity. The three approaches for computing snowmelt, namely, the degree-day, restricted degree-day, and daily energy balance model were tested at the local scale by comparing melt rates with lysimeter outflow measurements. Because radiation measurements are not often available, a simple model for simulating shortwave and longwave components of the radiation balance that requires minimal information (i.e., daily cloud cover estimates, air temperature, and relative humidity) was developed. It was found that clouds and their effects on daily insolation at the surface can produce significant differences between measured and model estimates. In the comparisons of snowmelt estimates with the lysimeter outflow, the restricted degree-day method yielded melt rates that were in better agreement with the observed outflow than the degree-day method and were practically the same as estimates given by the energy balance model. A sensitivity analysis of runoff generated with SRM using as input the local snowmelt computations given by the three models and measured outflow from the lysimeter was performed for a basin. A comparison of the synthetic hydrographs for the basin suggests that a radiation-based snowmelt factor may improve runoff predictions at the basin scale.

De volgende aspekten zijn mede aan de hand van tabellen en grafieken uiteengezet: wat zoutschade inhoudt en waar het optreedt; het belang van zoutonderzoek; zoutschadeverschijnselen en gevolgen van zoutschade; methoden om zoutschade vast te stellen; tolerantie van populieren en wilgen tegen zout in straat- en wegbeplantingen, alsook tegen brak en zout grondwater. Zowel over de invloed van zout op de groei van populieren en wilgen, alsook over de aanplantmogelijkheden is nog weinig informatie voorhanden

Zoutbelasting bodem onderdeel Gelderse Vallei

no abstract