Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2
Super, Ingrid ; Denier Van Der Gon, Hugo A.C. ; Molen, Michiel K. Van Der; Dellaert, Stijn N.C. ; Peters, Wouter - \ 2020
Geoscientific Model Development 13 (2020)6. - ISSN 1991-959X - p. 2695 - 2721.
We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx , and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N D 44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1km-1km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences < 2 %, hourly temporal r2 D 0:21-0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced.
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Pastorello, Gilberto ; Trotta, Carlo ; Canfora, Eleonora ; Chu, Housen ; Christianson, Danielle ; Cheah, You Wei ; Poindexter, Cristina ; Chen, Jiquan ; Elbashandy, Abdelrahman ; Humphrey, Marty ; Isaac, Peter ; Polidori, Diego ; Ribeca, Alessio ; Ingen, Catharine van; Zhang, Leiming ; Amiro, Brian ; Ammann, Christof ; Arain, M.A. ; Ardö, Jonas ; Arkebauer, Timothy ; Arndt, Stefan K. ; Arriga, Nicola ; Aubinet, Marc ; Aurela, Mika ; Baldocchi, Dennis ; Barr, Alan ; Beamesderfer, Eric ; Marchesini, Luca Belelli ; Bergeron, Onil ; Beringer, Jason ; Bernhofer, Christian ; Berveiller, Daniel ; Billesbach, Dave ; Black, Thomas Andrew ; Blanken, Peter D. ; Bohrer, Gil ; Boike, Julia ; Bolstad, Paul V. ; Bonal, Damien ; Bonnefond, Jean Marc ; Bowling, David R. ; Bracho, Rosvel ; Brodeur, Jason ; Brümmer, Christian ; Buchmann, Nina ; Burban, Benoit ; Burns, Sean P. ; Buysse, Pauline ; Cale, Peter ; Cavagna, Mauro ; Cellier, Pierre ; Chen, Shiping ; Chini, Isaac ; Christensen, Torben R. ; Cleverly, James ; Collalti, Alessio ; Consalvo, Claudia ; Cook, Bruce D. ; Cook, David ; Coursolle, Carole ; Cremonese, Edoardo ; Curtis, Peter S. ; Andrea, Ettore D'; Rocha, Humberto da; Dai, Xiaoqin ; Davis, Kenneth J. ; Cinti, Bruno De; Grandcourt, Agnes de; Ligne, Anne De; Oliveira, Raimundo C. De; Delpierre, Nicolas ; Desai, Ankur R. ; Bella, Carlos Marcelo Di; Tommasi, Paul di; Dolman, Han ; Domingo, Francisco ; Dong, Gang ; Dore, Sabina ; Duce, Pierpaolo ; Dufrêne, Eric ; Dunn, Allison ; Dušek, Jiří ; Eamus, Derek ; Eichelmann, Uwe ; ElKhidir, Hatim Abdalla M. ; Eugster, Werner ; Ewenz, Cacilia M. ; Ewers, Brent ; Famulari, Daniela ; Fares, Silvano ; Feigenwinter, Iris ; Feitz, Andrew ; Fensholt, Rasmus ; Filippa, Gianluca ; Fischer, Marc ; Frank, John ; Galvagno, Marta ; Gharun, Mana ; Gianelle, Damiano ; Gielen, Bert ; Gioli, Beniamino ; Gitelson, Anatoly ; Goded, Ignacio ; Goeckede, Mathias ; Goldstein, Allen H. ; Gough, Christopher M. ; Goulden, Michael L. ; Graf, Alexander ; Griebel, Anne ; Gruening, Carsten ; Grünwald, Thomas ; Hammerle, Albin ; Han, Shijie ; Han, Xingguo ; Hansen, Birger Ulf ; Hanson, Chad ; Hatakka, Juha ; He, Yongtao ; Hehn, Markus ; Heinesch, Bernard ; Hinko-Najera, Nina ; Hörtnagl, Lukas ; Hutley, Lindsay ; Ibrom, Andreas ; Ikawa, Hiroki ; Jackowicz-Korczynski, Marcin ; Janouš, Dalibor ; Jans, Wilma ; Jassal, Rachhpal ; Jiang, Shicheng ; Kato, Tomomichi ; Khomik, Myroslava ; Klatt, Janina ; Knohl, Alexander ; Knox, Sara ; Kobayashi, Hideki ; Koerber, Georgia ; Kolle, Olaf ; Kosugi, Yoshiko ; Kotani, Ayumi ; Kowalski, Andrew ; Kruijt, Bart ; Kurbatova, Julia ; Kutsch, Werner L. ; Kwon, Hyojung ; Launiainen, Samuli ; Laurila, Tuomas ; Law, Bev ; Leuning, Ray ; Li, Yingnian ; Liddell, Michael ; Limousin, Jean Marc ; Lion, Marryanna ; Liska, Adam J. ; Lohila, Annalea ; López-Ballesteros, Ana ; López-Blanco, Efrén ; Loubet, Benjamin ; Loustau, Denis ; Lucas-Moffat, Antje ; Lüers, Johannes ; Ma, Siyan ; Macfarlane, Craig ; Magliulo, Vincenzo ; Maier, Regine ; Mammarella, Ivan ; Manca, Giovanni ; Marcolla, Barbara ; Margolis, Hank A. ; Marras, Serena ; Massman, William ; Mastepanov, Mikhail ; Matamala, Roser ; Matthes, Jaclyn Hatala ; Mazzenga, Francesco ; McCaughey, Harry ; McHugh, Ian ; McMillan, Andrew M.S. ; Merbold, Lutz ; Meyer, Wayne ; Meyers, Tilden ; Miller, Scott D. ; Minerbi, Stefano ; Moderow, Uta ; Monson, Russell K. ; Montagnani, Leonardo ; Moore, Caitlin E. ; Moors, Eddy ; Moreaux, Virginie ; Moureaux, Christine ; Munger, J.W. ; Nakai, Taro ; Neirynck, Johan ; Nesic, Zoran ; Nicolini, Giacomo ; Noormets, Asko ; Northwood, Matthew ; Nosetto, Marcelo ; Nouvellon, Yann ; Novick, Kimberly ; Oechel, Walter ; Olesen, Jørgen Eivind ; Ourcival, Jean Marc ; Papuga, Shirley A. ; Parmentier, Frans Jan ; Paul-Limoges, Eugenie ; Pavelka, Marian ; Peichl, Matthias ; Pendall, Elise ; Phillips, Richard P. ; Pilegaard, Kim ; Pirk, Norbert ; Posse, Gabriela ; Powell, Thomas ; Prasse, Heiko ; Prober, Suzanne M. ; Rambal, Serge ; Rannik, Üllar ; Raz-Yaseef, Naama ; Reed, David ; Dios, Victor Resco de; Restrepo-Coupe, Natalia ; Reverter, Borja R. ; Roland, Marilyn ; Sabbatini, Simone ; Sachs, Torsten ; Saleska, Scott R. ; Sánchez-Cañete, Enrique P. ; Sanchez-Mejia, Zulia M. ; Schmid, Hans Peter ; Schmidt, Marius ; Schneider, Karl ; Schrader, Frederik ; Schroder, Ivan ; Scott, Russell L. ; Sedlák, Pavel ; Serrano-Ortíz, Penélope ; Shao, Changliang ; Shi, Peili ; Shironya, Ivan ; Siebicke, Lukas ; Šigut, Ladislav ; Silberstein, Richard ; Sirca, Costantino ; Spano, Donatella ; Steinbrecher, Rainer ; Stevens, Robert M. ; Sturtevant, Cove ; Suyker, Andy ; Tagesson, Torbern ; Takanashi, Satoru ; Tang, Yanhong ; Tapper, Nigel ; Thom, Jonathan ; Tiedemann, Frank ; Tomassucci, Michele ; Tuovinen, Juha Pekka ; Urbanski, Shawn ; Valentini, Riccardo ; Molen, Michiel van der; Gorsel, Eva van; Huissteden, Ko van; Varlagin, Andrej ; Verfaillie, Joseph ; Vesala, Timo ; Vincke, Caroline ; Vitale, Domenico ; Vygodskaya, Natalia ; Walker, Jeffrey P. ; Walter-Shea, Elizabeth ; Wang, Huimin ; Weber, Robin ; Westermann, Sebastian ; Wille, Christian ; Wofsy, Steven ; Wohlfahrt, Georg ; Wolf, Sebastian ; Woodgate, William ; Li, Yuelin ; Zampedri, Roberto ; Zhang, Junhui ; Zhou, Guoyi ; Zona, Donatella ; Agarwal, Deb ; Biraud, Sebastien ; Torn, Margaret ; Papale, Dario - \ 2020
Scientific Data 7 (2020)1. - ISSN 2052-4463 - 1 p.
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands
Lu, Meng ; Soenario, Ivan ; Helbich, Marco ; Schmitz, Oliver ; Hoek, Gerard ; Molen, Michiel van der; Karssenberg, Derek - \ 2020
Atmospheric Environment 223 (2020). - ISSN 1352-2310
Land use regression (LUR) modeling has been applied to study the spatiotemporal patterns of air pollution, which when combined with human space-time activity, is important in understanding the health effects of air pollution. However, most of these studies focus either on the temporal or the spatial domain and do not consider the variability in both space and time. A temporally aggregated model does not reflect the temporal variability caused by traffic and atmospheric conditions and leads to inaccurate estimation of personal exposure. Besides, most studies focus on a single air pollutant (e.g., O3, NO2, or NO). These pollutants have a strong interaction due to photochemical processes. For studying relations between spatial and temporal patterns in these pollutants it is preferable to use a uniform data source and modelling approach which makes comparison of pollution surfaces between pollutants more reliable as they are produced with the same methodology. We developed temporal land use regression models of O3, NO2 and NO to study the co-variability of these pollutants and the relations with typical weather conditions over the year. We use hourly concentrations from the measurement network of the Dutch National Institute for Public Health and the Environment and aggregate them by hour, for weekday/weekend and month, and fit a regression model for each hour of the day. 70 candidate predictors that are known to have a strong relationship with combustion-related emissions are evaluated in the LUR modelling process. For all pollutants, the optimal LUR was identified with 4 predictors and the temporal variability was determined by the explained variance of each temporal model. Our temporal models for O3, NO2, and NO strongly reflect the photochemical processes in space and time. O3 shows a high background value throughout the day and only dips in the (close) vicinity of roads. The diminishing rate is affected by traffic intensity. The NO2 LUR is validated against NO2 measurements from the Traffic-Related Air pollution and Children's respiratory HEalth and Allergies (TRACHEA) study, resulting in an R2 of 0.61.
Het Markermeer: een molen zonder wieken, hoe maken we daar een robuust ecosysteem van?
Verdonschot, Piet - \ 2019
Samen met partner EMS Films is een reeks Natuurcolleges gemaakt. In deze Natuurcolleges behandelen verschillende wetenschappers, vertegenwoordigers uit het bedrijfsleven en NGO’s een onderwerp uit het LIFE IP Deltanatuur programma.
Weather and crop dynamics in a complex terrain, the Gamo Highlands – Ethiopia : Towards a high-resolution and model-observation based approach
Minda, Thomas Torora - \ 2019
Wageningen University. Promotor(en): J. Vilà-Guerau de Arellano; P.C. Struik, co-promotor(en): M.K. van der Molen. - Wageningen : Wageningen University - ISBN 9789463950664 - 200
Motivation: Ethiopia is one of the Sub-Saharan countries that are strongly influenced by climate fluctuations. These meteorological changes directly affect agriculture and consequently cause disturbances on the regional and local economy. To pinpoint a few crucial issues: (1) the agricultural sector in Ethiopia accounts for 80% of the employment and contributes 45% of the GDP. A relevant factor in relation to this PhD thesis is that the country’s agriculture is by 95% rainfed agronomy. (2) The Ethiopian landscape is composed of complex terrains of the East African mountain system – the Ethiopian Highlands (40% of the Ethiopia’s landmass is elevated more than 1500 m above sea level). This complex orography modulates weather and climate at scales ranging from local to regional. In the region, weather dynamics are mainly driven by both synoptic (e.g. Intertropical Convergence Zone – ITCZ) and mesoscale flows (e.g. lake and mountain breezes). These weather scales ultimately influence the way crops grow. The aim of this study was to evaluate how weather and crop growth vary in a complex terrain and heterogeneous landscape. I focus on the Gamo Highlands, south-west Ethiopia, a mountainous region with two large Rift-Valley lakes in Ethiopia. The crop of interest was potato – a crop that has become popular in Ethiopia, significantly contributing to food security and income, but sensitive to climatic variations. As a research method, I deployed a high-resolution weather and crop modelling approach to describe how the growth and yield of the potato crop depend on the variations in weather. For observation-based studies and for testing the models’ performance, six automatic weather stations were installed and field crop experiments were conducted near the stations. More specifically, this thesis addresses the role of meteorological crop drivers (e.g. the incoming shortwave radiation (SW↓), maximum temperature (Tmax), minimum temperature (Tmin) and precipitation (PPT)) and edaphic variables (soil moisture and soil temperature) on the yield and growth of the Ethiopian potato cultivars.
Research methods and findings: In Chapter 1, I reviewed the contemporary global environmental challenge, the Anthropocene geologic era, in relation to the food system in perspective. In this chapter, I cascaded the problem from the global to the local scale. The chapter argued that the global weather models need to be downscaled to the local scales in order to study weather and climate impacts on crop dynamics in complex topographic landscapes such as Ethiopia.
In Chapters 2 and 3, I presented the model-observation combined research strategy implemented in this thesis. The temporal and spatial variations in weather and crop dynamics are analysed using data from 2001 to 2010. To this end, the Weather Research and Forecasting (WRF) model is used to simulate weather at coarse (54 × 54 km2) and fine (2 × 2 km2) resolutions during the 10-years. The model is validated with in situ data. The meteorological crop growth drivers (SW↓, Tmax, Tmin, PPT, vapour pressure deficit and wind speed) and soil data from the ISRIC soil database are supplied as inputs to a process-based crop model called GECROS. The 10-year belg seasons WRF model analysis is showed large temporal and spatial variabilities in SW↓, Tmax, Tmin and PPT in the Gamo Highlands. For example, Tmax ranged from 10 °C on the summit of mount Guge to 30 °C in the valley around Lake Abaya and Lake Chamo. Temporally, the belg season of 2006 is identified as climatologically normal whilst the 2008 (driest) and 2010 (wettest) belg seasons are categorized as anomalous years. The temporal variations in simulated attainable potato yield showed a high yield (~20 to 30 t ha-1) during the normal belg season whereas the yield was lower (5 to 10 t ha-1 less than in the normal year) for the anomalous belg seasons (Chapter 2). As compared to the coarse resolution domain, the fine resolution domain is better represented topography and weather variations. Because of the improved representation of topography and weather in the fine resolution domain, the leaf area index (LAI) and the length of the growing season (LGS) simulated by the GECROS model were in the recommended range for potato (LAI of 3 m2 m-2 and LGS of 120 days are simulated). For comparison, modelled values were unacceptably low in the coarse resolution domain (LAI of 1.0 m2 m-2 and LGS of 60 days). It is also interesting to see that temperature and precipitation played opposing roles in the modelled yield, a phenomenon I called a compensating effect. To explain the term, moving up the mountains, the temperature decreases – with a positive effect on yield, and precipitation increases with a negative effect on yield. The lower temperature at higher elevation increases the LGS; as a result, more carbon is allocated to the tubers than in a shorter growing season. The higher precipitation at higher elevation may give rise to soil nutrient loss caused by leaching. Aloft the highlands, temperature and PPT are showed opposite trends, but their effects are balanced out in the ultimate yield (Chapter 3).
Chapter 4 presented the Gamo Highlands Meteorological Stations (GEMS) – a network of six automatic weather stations, which were operational since April 2016 in two transects of the highlands. Near to the GEMS network, potato field trials are conducted. I used the GEMS data to study both the mesoscale and synoptic weather scales influencing the Gamo Highlands. Furthermore, I deployed the in situ data to the GECROS crop model. The GEMS data are analysed for belg-2017 showed major differences between the start (February) and the end (May) of the belg season. February and May are more mesoscale and synoptic scale weather system dominated months, respectively. During February, the day-night wind sources showed strong variation. Strong south to south-easterly lake breezes are observed during daytime; whereas, weak and more localised mountain winds are identified during the night-time. In May, the day-night flow contrast was small and the dominant flows were southerly. The location of ITCZ calculated by the NOAA (National Oceanic and Atmospheric Administration) and the GEMS observed sea-level-pressure (SLP) data showed strong correlation. My analysis showed that the low-pressure system (ITCZ) and the rainbelt are not coincided in the Gamo Highlands. The maximum PPT is received in May where the ITCZ is located on average nearly 6° (north) away from Gamo Highlands. During the maximum PPT in May 2017, the southerly moist air masses from the moisture sources (e.g. Indian Ocean) may move to the low-pressure system located to the north of the study area. During the daytime, PPT is less probable as cloud formation was less likely due to the enhanced solar radiation. However, during night-time, the southerly moisture can be trapped in the highlands and orographic PPT can be triggered. This PPT is locally modulated due to the presence of the Gamo Highlands and presence of the lakes. The moisture is crucial for potato agronomy during the belg season. The GECROS model sensitivity analysis, using the GEMS data, showed that model input of constant PPT (belg-averaged) gave the highest crop yield due to improved soil moisture throughout the growing season.
Chapter 5 dealt with investigating the role of environmental factors on potato yield and growth in the Gamo Highlands. Here, the GEMS weather and edaphic data are correlated with crop growth variables such as plant height, canopy cover, yield and yield traits. The GEMS and crop observation datasets showed that plant height and canopy cover are strongly correlated with temperature sum (Tsum) with an r2 > 0.95 during the canopy buildup phase (P1). Tsum (d °C) is defined as the sum of the daily average temperatures during the growing season. The crop growth - Tsum correlation is further explained in terms of SW↓ and soil moisture, in which an improved (Gudene) and a local (Suthalo) cultivar showed different responses to SW↓ and soil moisture regimes. Data also showed that tuber yield is poorly explained by meteorological and edaphic data, suggesting further research activity in this regard. When the number of days to crop maturity was between 100-110 days, an optimal tuber yield is obtained.
Chapter 6 presented the main findings of the thesis in perspective. Finally, Chapter 7 discussed the key findings in-line with the research questions stated in Chapter 1.
Conclusions and perspectives: In complex terrain, weather/climate varies over short distances affecting crop growth. To describe crop growth and yield in the region, a high-resolution weather model, coupled to a crop model is needed. The weather model outputs can be used as input to the crop model. A dense station network installed in a complex topographic region can give us insights on mesoscale flows (e.g. lake-mountain flows), synoptic systems (e.g. south-north movement of the ITCZ) and crop growth (e.g. LGS and LAI). Additional weather stations (e.g. on the lee-side of the Gamo Highlands and east of the Lakes Abaya and Chamo) can give us improved understanding of weather scales and crop growth. Tsum during the P1 is found to be a good predictor of plant height and canopy cover for the Ethiopian potato cultivars. The poor correlation between environmental variables and yield and yield traits suggests more dedicated field experiments should be designed. One of the suggested field experiments is continuous monitoring of the partitioning of dry matter to the tubers to study how crop yield varies as a function of elevation and meteorology.
Nitrogen Deposition Maintains a Positive Effect on Terrestrial Carbon Sequestration in the 21st Century Despite Growing Phosphorus Limitation at Regional Scales
Fleischer, Katrin ; Dolman, A.J. ; Molen, Michiel K. van der; Rebel, Karin T. ; Erisman, Jan Willem ; Wassen, Martin J. ; Pak, Bernard ; Lu, Xingjie ; Rammig, Anja ; Wang, Ying Ping - \ 2019
Global Biogeochemical Cycles 33 (2019)6. - ISSN 0886-6236 - p. 810 - 824.
carbon sequestration - land carbon sink - nitrogen deposition - nitrogen fixation - phosphorus limitation - terrestrial ecosystems
Nitrogen (N) and phosphorus (P) are two dominant nutrients regulating the productivity of most terrestrial ecosystems. The growing imbalance of anthropogenic N and P inputs into the future is estimated to exacerbate P limitation on land and limit the land carbon (C) sink, so that we hypothesized that P limitation will increasingly reduce C sequestered per unit N deposited into the future. Using a global land surface model (CABLE), we simulated the effects of increased N deposition with and without P limitation on land C uptake and the fate of deposited N on land from 1901 to 2100. Contrary to our hypothesis, we found that N deposition continued to induce land C sequestration into the future, contributing to 15% of future C sequestration as opposed to 6% over the historical period. P limitation reduced the future land C uptake per unit N deposited only moderately at the global scale but P limitation increasingly caused N deposition to have net negative effects on the land C balance in the temperate zone. P limitation further increased the fraction of deposited N that is lost via leaching to aquatic ecosystems, globally from 38.5% over the historical period to 53% into the future, and up to 75% in tropical ecosystems. Our results suggest continued N demand for plant productivity but also indicate growing adverse N deposition effects in the future biosphere, not fully accounted for in global models, emphasizing the urgent need to elaborate on model representations of N and P dynamics.
Correction: Responses of canopy growth and yield of potato cultivars to weather dynamics in a complex topography: Belg farming seasons in the gamo highlands, Ethiopia
Minda, Thomas T. ; Molen, Michiel K. Van Der; Arellano, Jordi Vilà Guerau De; Chulda, Kanko C. ; Struik, Paul C. - \ 2019
Agronomy 9 (2019)5. - ISSN 2073-4395
In Minda et al. , an error was introduced. We propose the following amendment: Figure 9, in Section 3.2.4 (Days to Maturity and Yield), should be replaced by the following updated figure. (Figure Presented) The authors apologize for any inconvenience caused to the readers by these changes. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction.
Global challenges, Dutch solutions? The shape of responsibility in Dutch science and technology policies
Molen, Franke van der; Ludwig, David ; Consoli, Luca ; Zwart, Hub - \ 2019
Journal of Responsible Innovation 6 (2019)3. - ISSN 2329-9460 - p. 340 - 345.
Responsible research and innovation - science and technology policy - the Netherlands
The Netherlands has a well-established tradition of gearing science and technology to economic interests as well as societal and ethical concerns. This article outlines how national dynamics in the Netherlands have not only contributed to the adoption of Responsible Research and Innovation (RRI) frameworks but also to a distinctly Dutch meaning and institutionalization of responsibility. It identifies three core features of the Dutch context that have shaped this meaning and institutionalization: 1) a strong focus on the societal and economic relevance of research and innovation, 2) a political culture that emphasizes inclusive deliberation and collaboration, and 3) a focus on integration and synergy with respect to RRI. The integration of RRI in a collaborative system of companies, government and universities is embraced as contributing to a global leadership of the Netherlands in response to grand challenges. However, this integrative approach also limits the potential of Dutch RRI to function as a disruptive concept that challenges the status of interactions between science, technology, and society.
Responses of Canopy Growth and Yield of Potato Cultivars to Weather Dynamics in a Complex Topography: Belg Farming Seasons in the Gamo Highlands, Ethiopia
Minda, Thomas T. ; Molen, Michiel K. Van Der; Vilà-Guerau de Arellano, Jordi ; Chulda, Kanko C. ; Struik, Paul C. - \ 2019
Agronomy 9 (2019)4. - ISSN 2073-4395 - 27 p.
Potato is an increasingly important crop in Ethiopia. The Gamo Highlands are one of the large potential potato producing regions in Ethiopia. The growing conditions are different from those in the temperate regions, where most of the agronomical expertise on potato has been developed. The influence of environmental conditions on the crop in the Gamo Highlands is poorly understood. We conducted field trials with eight potato cultivars in six locations and during two seasons. The canopy cover (CC) and plant height (PH) were measured with high temporal resolution and tuber yields were assessed as well. The experiments were conducted near our newly installed weather stations at different elevations. CC and PH were strongly correlated with temperature sum (Tsum). Tuber yields differed among elevations and cultivars. Nevertheless, these differences were poorly explained by environmental variables. We also found that no single cultivar performed best at all elevations. The number of branches was a predictor of yield, suggesting that radiation interception was limiting tuber growth. Tuber yield was optimal when the number of days to crop maturity was around 100–110 days. We conclude that Tsum is a predictor of crop growth, but environmental variables poorly explain yield variations, which calls for further investigation
A remote cis-regulatory region is required for nin expression in the pericycle to initiate nodule primordium formation in medicago truncatula
Liu, Jieyu ; Rutten, Luuk ; Limpens, Erik ; Molen, Tjitse Van Der; Velzen, Robin Van; Chen, Rujin ; Chen, Yuhui ; Geurts, Rene ; Kohlen, Wouter ; Kulikova, Olga ; Bisseling, Ton - \ 2019
The Plant Cell 31 (2019)1. - ISSN 1040-4651 - p. 68 - 83.
The legume-rhizobium symbiosis results in nitrogen-fixing root nodules, and their formation involves both intracellular infection initiated in the epidermis and nodule organogenesis initiated in inner root cell layers. NODULE INCEPTION (NIN) is a nodule-specific transcription factor essential for both processes. These NIN-regulated processes occur at different times and locations in the root, demonstrating a complex pattern of spatiotemporal regulation. We show that regulatory sequences sufficient for the epidermal infection process are located within a 5 kb region directly upstream of the NIN start codon in Medicago truncatula. Furthermore, we identify a remote upstream cis-regulatory region required for the expression of NIN in the pericycle, and we show that this region is essential for nodule organogenesis. This region contains putative cytokinin response elements and is conserved in eight more legume species. Both the cytokinin receptor 1, which is essential for nodule primordium formation, and the B-type response regulator RR1 are expressed in the pericycle in the susceptible zone of the uninoculated root. This, together with the identification of the cytokinin-responsive elements in the NIN promoter, strongly suggests that NIN expression is initially triggered by cytokinin signaling in the pericycle to initiate nodule primordium formation.
|Responsible Research and Innovation in Practice
Molen, Franke van der; Consoli, Luca ; Ludwig, D.J. ; Macnaghten, Philip - \ 2018
Nijmegen : Radboud Universiteit - 25 p.
Referenties en maatlatten voor natuurlijke watertypen voor de Kaderrichtlijn Water 2021-2027
Altenburg, W. ; Arts, G. ; Baretta-Bekker, J.G. ; Berg, M.S. van den; Broek Broek, T. van den; Buskens, R. ; Bijkerk, R. ; Coops, H.C. ; Dam, H. van; Ee, G. van; Evers, C.H.M. ; Franken, R. ; Higler, B. ; Ietswaart, T. ; Jaarsma, N. ; Jong, D.J. de; Joosten, A.M.T. ; Klinge, M. ; Knoben, R.A.E. ; Kranenbarg, J. ; Loon, W.M.G.M. van; Noordhuis, R. ; Pot, R. ; Twisk, F. ; Verdonschot, P.F.M. ; Vlek, H. ; Backx, J.J.G.M. ; Beers, M. ; Buijse, A.D. ; Duursema, G. ; Fagel, M. ; Leeuw, J. de; Molen, J. van der; Nijboer, R.C. ; Postma, J. ; Vriese, T. ; Duijts, R. ; Hartholt, J.G. ; Jager, Z. ; Stikvoort, E.C. ; Walvoort, D. - \ 2018
Amersfoort : Stowa (Stowa rapport 2018-49) - ISBN 9789057738135 - 481
Observational characterization of the Synoptic and Mesoscale circulations in Relation to Crop Dynamics: Belg 2017 in the Gamo Highlands, Ethiopia
Minda, T.T. ; Molen, M.K. van der; Heusinkveld, B.G. ; Struik, P.C. ; Vilà-Guerau de Arellano, J. - \ 2018
Atmosphere 9 (2018)10. - ISSN 2073-4433 - 24 p.
The Gamo Highlands in Ethiopia are characterized by complex topography and lakes. These modulate the mesoscale and synoptic scale weather systems. In this study, we analyzed the temporal and spatial variations in weather as function of topography and season and their impact on potato crop growth. To determine how crop growth varies with elevation, we installed a network of six automatic weather stations along two transects. It covers a 30-km radius and 1800-m elevation difference. We conducted a potato field experiment near the weather stations. We used the weather observations as input for a crop model, GECROS. Data analysis showed large differences between weather in February and May. February is more dominated by mesoscale circulations. The averaged February diurnal patter shows a strong east to southeast lake breezes and, at night, weak localized flows driven by mountain density flows. In contrast, in May, the synoptic flow dominates, interacting with the mesoscale flows. The GECROS model satisfactorily predicted the elevational gradient in crop yield. Model sensitivity experiments showed that belg-averaged precipitation distribution gave the highest yield, followed by exchanging May weather observations with April.
Quantification and attribution of urban fossil fuel emissions through atmospheric measurements
Super, Ingrid - \ 2018
Wageningen University. Promotor(en): W. Peters, co-promotor(en): M.K. van der Molen; H.A.C. Denier van der Gon. - Wageningen : Wageningen University - ISBN 9789463434980 - 192
Fossil fuel combustion causes an increase in atmospheric carbon dioxide (CO2) levels and is one of the major causes of climate change. Therefore, efforts are made to reduce CO2 emissions from fossil fuel combustion through (inter)national agreements, with the most famous example being the Paris agreement. Each member state that ratified the agreement has to aim for pre-set emission reduction targets. In this collaborative effort it is important to keep track of the progress made towards these targets, but also to gain insight in which emission reduction policies are most effective to support future decision-making. Therefore, scholars have started developing atmospheric monitoring techniques, mainly focused on urban areas. Since about 70% of the anthropogenic CO2 emissions takes place in urban areas, the largest emission reductions will take place here. This causes large atmospheric signals that are relatively easy to measure. However, scholars have faced some major challenges. For example, the transport within a built-up area is complex, making the interpretation of atmospheric observations difficult. Moreover, emission reduction policies often target specific source sectors (such as road traffic or industry). Hence, these sectors should be monitored separately to understand the effectiveness of individual measures. This source attribution is impossible with only CO2 observations when source sectors are not spatially isolated.
The overall aim of this thesis is to improve our understanding of the monitoring requirements to constrain urban fossil fuel CO2 emissions per source sector. A key feature of a monitoring system is a network of observation sites. Therefore, the first research objective is to identify the most useful monitoring sites and network configurations. Besides CO2 we also included measurements of trace gasses that are co-emitted with CO2 during fossil fuel combustion. This happens in a ratio that is specific for a source sector and therefore these tracers have the potential to identify the source of a CO2 signal. We examined this opportunity to use co-emitted species to attribute CO2 signals to specific source sectors. Besides observations a good model representation of atmospheric transport is needed to interpret the observations. Therefore, the second research objective is to better understand the possibilities and limitations of atmospheric transport models in reproducing observed mixing ratios within/close to a city and find a useful modelling approach. The third objective is to predict high-resolution emissions in an urban area using proxy data and to gain insight in the uncertainties related to these emissions. Finally, we combine our insights related to measurements, models and emission modelling into an inversion framework to estimate how well we can constrain urban CO2 emissions per source sector (objective 4).
Results and conclusions
In Chapter 2 we examined the effectiveness of two observation sites close to the city border of Rotterdam, providing a gradient in the CO2 mixing ratio over the city from the upwind to the downwind site. The two sites provide one year of hourly mixing ratio gradients which are used to make a first estimate of the urban emissions. For this purpose we first examined whether the upwind site was representative for the composition of the background signal, which proved to be the case for specific wind directions. We found on average large enhancements at the downwind site compared to the upwind site for three major source areas: the city, the port and the glasshouse area. From the selected gradients we calculated emissions, accounting only for average biospheric fluxes, footprints, and boundary layer height. Although this approach is very simplified it shows reasonable flux estimates compared to the reported emissions. Nevertheless, we found that the estimates can be heavily influenced by local emissions and by transport processes that we could not take into account. For example, the presence of elevated stack emissions complicates the estimate of the emissions without detailed knowledge of the atmospheric transport. Finally, the results show that CO can potentially attribute a CO2 signal to industrial or residential source areas. We conclude that observed mixing ratio gradients can be used to make a rough estimate of the urban emissions, in which CO is of added value to identify dominant source types.
In Chapter 3 we compared two atmospheric transport models: the Eulerian WRF-Chem model (1x1 km2 resolution) and the Lagrangian OPS model. Atmospheric transport models are useful to account for the impact of transport, mixing, entrainment, and biospheric fluxes on the observed mixing ratios and can help interpret the observed signals. We examined the ability of these models to reproduce the observed mixing ratios at several measurement sites along a transect from an urban (Rotterdam) to rural location. On average, WRF-Chem gives good results, reproducing meso-scale features with the correct order of magnitude for the observed CO2 mixing ratios. However, the timing of CO2 mixing ratio enhancements is often incorrect, which is mainly the result of an incorrect representation of the wind direction causing the model to sample the wrong source area. Moreover, we found that the representation of point sources is problematic. In a Eulerian model emissions get instantly mixed throughout the grid box, which causes a large underestimation of local and downwind mixing ratios for sources with a small horizontal extent. Using the OPS model improves the representation of point sources, because it has no spatial discretization. The difference between OPS and WRF-Chem is only visible up to approximately 15 km from major stack emissions, such that point sources further away from observation sites can be represented by WRF-Chem as well. An additional advantage of the OPS model is that it can be driven by locally observed meteorological data, such that it overcomes the wind direction issue from WRF-Chem. However, the OPS model is sub-optimal for area source emissions over a large domain and therefore we conclude that a combination of both models is the best option in Rotterdam. Finally, the results in Chapter 3 show that urban sites are well-exposed to urban fossil fuel fluxes and can be used to separate between different source areas (such as the residential and industrial area), especially if besides CO2 also CO is included. Sites that are further removed from the city (semi-urban) provide a better constraint on the total flux.
Chapter 4 explored the potential of several data streams to predict high-resolution emissions. These data were combined in a dynamic fossil fuel emission model that estimates emissions based on additional knowledge about the emission landscape. First, we calculated the total yearly emissions for the Netherlands per source sector using activity data (such as Gross Domestic Product), emission factors (the amount of CO2 emitted per amount of fuel consumed) and energy efficiency (amount of fuel consumed per amount of activity). Then the total yearly emissions were disaggregated to hourly and 1x1 km2 scale using proxies and hourly activity data. In this way we created a dynamic emission map based on a wide range of parameters that are specified per source sector. One major advantage is that we can estimate the (unknown) uncertainty in the high-resolution emissions from the (better-known) uncertainty in the model parameters. We find that we can estimate the yearly emissions for the Netherlands with a 15% uncertainty when using generalized proxies (i.e. based on general, large-scale activity data and emission factors). Using more specific knowledge about the region (e.g. about technological advancement) and local activity data reduces this uncertainty. We can also use the emission model to calculate emissions of co-emitted species by multiplying the CO2 emissions with the typical emission ratios for each source sector. These emission ratios are variable and uncertain and the emissions of co-emitted species have a larger uncertainty than the CO2 emission. Finally, the model parameters have a physical meaning and can be linked to emission reduction policies, making it a useful tool for policy-makers.
With the dynamic emission model we identified the most important and uncertain parameters affecting the emissions (CO2 emission factors, emission ratios and time profiles). In Chapter 5 we tried to optimize these parameters using a newly developed inverse modelling framework. The inversion system uses the multi-model framework described in Chapter 3 to translate the emissions calculated by the dynamic emission model into mixing ratios of CO2, CO, NOx (nitrogen oxides) and SO2 (sulphur dioxide). We used the same modelling framework to create pseudo-observations, which are used to validate the model. The only difference is the values appointed to the parameters in the emission model (generalized data for the prior, local data for the pseudo-observations). We performed an experiment to explore the difference between an urban and a rural observation network, which shows that the CO2 signals captured by the rural network are too small to contain relevant information. The urban network performs well and gives a good estimate of the total yearly emissions for the Rotterdam area (5% error). When we included observations of the co-emitted tracers the emission estimate per source sector generally improves. Some sectors remain difficult to constrain, for example due to the lack of large enhancements or the lack of a clear emission ratio signature. The time profiles can also be constrained relatively well, at least the day-to-day variability. However, for households the error in the time profile gets aliased into the emission factor, causing the emission factor to be less well constrained. When we introduced erroneous atmospheric transport the results deteriorate drastically, especially for power plants and industry (i.e. point sources) which suffer most from the transport errors. We conclude that an inversion system with a dynamic emission model as a prior has great potential for monitoring urban emissions, but transport errors currently hamper its applicability to real observations.
This work contributed to a better understanding of the complexity of the urban fossil fuel emissions and what is needed to monitor this. Urban observations provide useful information and, depending on the size and shape of the monitoring network, can be used to constrain urban emissions in more or less detail. Observations of co-emitted species have the potential to attribute CO2 emissions to specific source sectors and are an important addition to our inversion framework. The dynamic fossil fuel emission model has several major advantages over a regular emission map, being flexible and physically meaningful. Although several challenges remain, the work described in this thesis is an important step in the development of urban monitoring capacities.
Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale
Peters, W. ; Velde, I.R. van der; Schaik, Erik van; Miller, John B. ; Ciais, Philippe ; Duarte, Henrique F. ; Laan-Luijkx, I.T. van der; Molen, M.K. van der; Scholze, M. ; Schaefer, Kevin ; Vidale, Pier Luigi ; Verhoef, Anne ; Wårlind, D. ; Zhu, Dan ; Tans, Pieter P. ; Vaughn, Bruce ; White, James W.C. - \ 2018
Nature Geoscience 11 (2018). - ISSN 1752-0894 - p. 744 - 748.
Severe droughts in the Northern Hemisphere cause a widespread decline of agricultural yield, the reduction of forest carbon uptake, and increased CO2 growth rates in the atmosphere. Plants respond to droughts by partially closing their stomata to limit their evaporative water loss, at the expense of carbon uptake by photosynthesis. This trade-off maximizes their water-use efficiency (WUE), as measured for many individual plants under laboratory conditions and field experiments. Here we analyse the 13C/12C stable isotope ratio in atmospheric CO2 to provide new observational evidence of the impact of droughts on the WUE across areas of millions of square kilometres and spanning one decade of recent climate variability. We find strong and spatially coherent increases in WUE along with widespread reductions of net carbon uptake over the Northern Hemisphere during severe droughts that affected Europe, Russia and the United States in 2001–2011. The impact of those droughts on WUE and carbon uptake by vegetation is substantially larger than simulated by the land-surface schemes of six state-of-the-art climate models. This suggests that drought-induced carbon–climate feedbacks may be too small in these models and improvements to their vegetation dynamics using stable isotope observations can help to improve their drought response.
The combined effect of elevation and meteorology on potato crop dynamics : A 10-year study in the Gamo Highlands, Ethiopia
Minda, Thomas T. ; Molen, M.K. van der; Struik, Paul C. ; Combe, Marie ; Jiménez, Pedro A. ; Khan, Muhammad S. ; Arellano, Jordi Vilà Guerau de - \ 2018
Agricultural and Forest Meteorology 262 (2018). - ISSN 0168-1923 - p. 166 - 177.
Complex terrain - High-resolution agrometeorological model - Inter-annual variability
Potato (Solanum tuberosum L.) is an important crop in the Gamo Highlands in Ethiopia. The region is characterised by a complex topography with large inter-annual weather variations, where potatoes grow in a range of altitudes between 1,600 and 3,200 m above sea level (a.s.l.). Traditional large-scale crop modelling studies only crudely represent the effect of complex topography, misrepresenting spatial variability in meteorology and potato growth in the region. Here, we investigate how weather influenced by topography affects crop growth. We used the Weather Research and Forecasting (WRF) model to simulate weather in relation to topography in coarse (54 km × 54 km) and fine (2 km × 2 km) resolution domains. The first has a resolution similar to those used by large-scale crop modelling studies that only crudely resolve the horizontal and vertical spatial effects of topography. The second realistically represents the most important topographical variations. The weather variables modelled in both the coarse and fine resolution domains are given as input to the GECROS model (Genotype-by-Environment interaction on CROp growth Simulator) to simulate the potato growth. We modelled potato growth from 2001 to 2010 and studied its inter-annual variability. This enabled us to determine for the first time in Ethiopia how variations in weather are linked to crop dynamics as a function of elevation at a fine resolution. We found that due to its finer representation of topography, weather and crop growth spatio-temporal variations were better represented in the fine than in the coarse resolution domain. The magnitude of crop growth variables such as Leaf Area Index (LAI) and Length of the Growing Season (LGS) obtained with weather from the coarse resolution domain were unrealistically low, hence unacceptable. Nevertheless, the resulting potato yields in the coarse resolution domain were comparable with the yields from the fine resolution domain. We explain this paradoxical finding in terms of a compensating effect, as the opposite effects of temperature and precipitation on yield compensated for each other along the major potato growing transect in the Gamo Highlands. These offsetting effects were also dependent on the correct estimations of the LGS, LAI. We conclude that a well-resolved representation of complex topography is crucial to realistically model meteorology and crop physiology in tropical mountainous areas.
|Wat je moet weten over luchtkwaliteit in je huis. Wat adem je in?
Molen, M.K. van der - \ 2018
|Operatie schone lucht
Molen, Michiel van der - \ 2018
The CarbonTracker Data Assimilation System for CO2 and δ13C (CTDAS-C13 v1.0) : Retrieving information on land-atmosphere exchange processes
Velde, Ivar R. Van Der; Miller, John B. ; Molen, Michiel K. Van Der; Tans, Pieter P. ; Vaughn, Bruce H. ; White, James W.C. ; Schaefer, Kevin ; Peters, Wouter - \ 2018
Geoscientific Model Development 11 (2018)1. - ISSN 1991-959X - p. 283 - 304.
To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues 13CO2/12CO2 (δ13C). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of 13CO2 and 12CO2 molecules removed from the atmosphere by plants is dependent on moisture conditions. The dual-species CTDAS system varies the net exchange fluxes of both 13CO2 and CO2 in ocean and terrestrial biosphere models to create an ensemble of 13CO2 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated 13CO2 and CO2 mole fractions (and thus δ13C) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system, we are able to estimate changes in terrestrial δ13C exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and δ13C constraints as done in this work. The additional constraints on surface CO2 exchange from δ13C observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance.
Scepsis over proef om stadslucht te zuiveren
Molen, M.K. van der - \ 2017