11 research outputs found
Assimilation of data in the imitative modeling of environmental processes by the method of minimizing corrective perturbations
Get PDFThe on-line correction of the vector of a dynamic model state variable by direct or indirect
measurements is a well-known problem of the theory of automatic control. It is called the
problem of data assimilation. Ecological models require specific approaches to this problem
in comparison to traditional methods used in hydrometeorology. The main difference is that
there are only rare observations on a limited number of indirect characteristics. The paper
presents an original approach to data assimilation for such cases. The main idea is inclusion
an additional term to the initial system, which describes the random external influences that
are not counted in the ideal model. Further, one can find a form of these perturbations that
minimizes the weighted sum of their integrated power and the norm of deviations of the observed
and theoretical characteristics at moments of measurement. Then the choice of weighting factor
allows us for comparative confidence to be reflected between the theoretical model and actual
measurements. Thus, the formal statement of the problem is written down as a problem of
optimal control. Examples of application of the proposed method for several simple models are
given. In the problem of the uniform motion of a material point, an analytical solution can be
obtained which, nonetheless, yields rather interesting results. The problem of data assimilation
for the Lotka—Volterra model has been solved numerically. It is shown that the proposed
method of “minimal perturbation” leads to the least “traumatic” correction of the ideal model
in comparison with the available alternative approaches; i. e., adaptive parameter identification
or backdating adjustment of the initial state. Refs 7. Figs 2
Stochastic modeling of sea ice concentration fields for assessment of navigation conditions along the Northern Sea Route
Get PDFArticle describes a probabilistic model (stochastic generator) of spatial-temporal variability of sea ice concentration. Values of the ice concentration are generated at the nodes of the spatial grid with 10 km resolution; the model time step is one day. The change in ice concentration with time (temporal variability) is modeled on the basis of a matrix of transient probabilities (discrete Markov chain), each row of which is a distribution function of the conditional probability of changes in the ice concentration. Spatial variability is determined by empirical probability fields, with which the observed changes in fields of the ice concentration are associated with known conditional probability distribution functions. To identify the parameters of the stochastic generator, satellite data from the OSI SAF project for the period 1987–2019 were used. The generator takes into account seasonal, interannual and climatic variability. Interannual and climatic variability are determined on the basis of a stochastic model of changes in the types of ice coverage. In order to verify the developed stochastic generator, we compared the statistical indicators of observed and calculated ice fields. The results showed that the fieldaverage absolute error of statistical characteristics of the ice concentration (mean and standard deviation) does not exceed 3.3%. The discrepancy between the correlation intervals of ice coverage calculated from the model and measured ice concentration fields does not exceed 2 days. The variograms of the modeled and observed fields have a similar form and close values. As an example, we determined the duration of navigation of Arc4 ice class ships between the Barents and Kara Seas using synthetic fields of the ice concentration reproduced by the stochastic generator
Biomass and grain yield estimation for winter cereals on different spatial scales
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Стохастическое моделирование полей сплочённости ледяного покрова для оценки условий плавания по трассе Северного морского пути
Get PDFArticle describes a probabilistic model (stochastic generator) of spatial-temporal variability of sea ice concentration. Values of the ice concentration are generated at the nodes of the spatial grid with 10 km resolution; the model time step is one day. The change in ice concentration with time (temporal variability) is modeled on the basis of a matrix of transient probabilities (discrete Markov chain), each row of which is a distribution function of the conditional probability of changes in the ice concentration. Spatial variability is determined by empirical probability fields, with which the observed changes in fields of the ice concentration are associated with known conditional probability distribution functions. To identify the parameters of the stochastic generator, satellite data from the OSI SAF project for the period 1987–2019 were used. The generator takes into account seasonal, interannual and climatic variability. Interannual and climatic variability are determined on the basis of a stochastic model of changes in the types of ice coverage. In order to verify the developed stochastic generator, we compared the statistical indicators of observed and calculated ice fields. The results showed that the fieldaverage absolute error of statistical characteristics of the ice concentration (mean and standard deviation) does not exceed 3.3%. The discrepancy between the correlation intervals of ice coverage calculated from the model and measured ice concentration fields does not exceed 2 days. The variograms of the modeled and observed fields have a similar form and close values. As an example, we determined the duration of navigation of Arc4 ice class ships between the Barents and Kara Seas using synthetic fields of the ice concentration reproduced by the stochastic generator.Описана созданная вероятностная модель пространственно-временнóй изменчивости сплочённости ледяного покрова. Временнáя связанность обеспечивается за счёт использования цепей Маркова, а пространственная – путём введения эмпирических полей вероятности. Модель учитывает синоптическую, сезонную, межгодовую и климатическую изменчивости ледяного покрова. Определение параметров стохастического генератора выполнено на основе архивных данных проекта OSI SAF. Верификация модели показала, что средняя по полю абсолютная ошибка статистических показателей сплочённости (среднее и стандартное отклонение) относительно исторических данных не превышает 1/3 балла. Автокорреляционные функции ледовитости и вариограммы отдельных полей сплочённости по модельным и фактическим данным имеют схожий вид. На основе результатов расчёта вероятностной модели полей сплочённости рассчитаны даты начала и окончания навигации судов ледового класса Arc4 между Баренцевым и Карским морями
Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe
Get PDFThe main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.OA-hybri
The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols
Get PDFClimate change is expected to alter a multitude of factors important to agricultural
systems, including pests, diseases, weeds, extreme climate events, water resources,
soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration
([CO2]), temperature, andwater (CTW) will be the primary drivers of change
in crop growth and agricultural systems. Therefore, establishing the CTW-change
sensitivity of crop yields is an urgent research need and warrants diverse methods
of investigation. Crop models provide a biophysical, process-based tool to investigate crop
responses across varying environmental conditions and farm management techniques,
and have been applied in climate impact assessment by using a variety of
methods (White et al., 2011, and references therein). However, there is a significant
amount of divergence between various crop models’ responses to CTW changes
(R¨otter et al., 2011). While the application of a site-based crop model is relatively
simple, the coordination of such agricultural impact assessments on larger scales
requires consistent and timely contributions from a large number of crop modelers,
each time a new global climate model (GCM) scenario or downscaling technique
is created. A coordinated, global effort to rapidly examine CTW sensitivity across
multiple crops, crop models, and sites is needed to aid model development and
enhance the assessment of climate impacts (Deser et al., 2012)..
Governance, agricultural production practices and nature's contributions to people in coffee socio-ecological systems
No full textIlustraciones, mapasLos paisajes productivos cafeteros (PPC) constituyen una fuente de alimento, de identidad cultural, vida social, valor estético y vida rural. Sin embargo, las complejas dinámicas en las que se encuentra inmersa la caficultura han generado una gran presión sobre los PPC, que compromete la provisión potencial de otras contribuciones de la naturaleza a las personas (NCP) y socavan las bases naturales de la misma caficultura. La intensificación de la caficultura contribuye a la creación de paisajes con una alta provisión de café a expensas de otras NCP (trade-off) mientras que, otras alternativas productivas pueden favorecer la provisión de diversas contribuciones. Estas alternativas productivas se viabilizan, entre otras formas, a través de la gobernanza dado que esta última puede orientar, por medio de las intervenciones de gobernanza, las decisiones de gestión agrícola hacia determinados modelos de producción. Con el propósito de contribuir al entendimiento de la incidencia de los modos de gobernanza en las prácticas producción agrícola y, por tanto, en la provisión potencial de otras NCP en un sistema-socio-ecológico (SSE) cafetero, en esta tesis se propuso, mediante una investigación de enfoque cualitativo, analizar la tipología de gobernanza y su incidencia sobre las prácticas y las contribuciones de la naturaleza. Para esto, se hizo necesario determinar los modos de gobernanza, las prácticas agrícolas adoptadas, las contribuciones de la naturaleza a las personas que son percibidas y las condiciones marco para la caficultura que influyen en las decisiones de adopción. Los hallazgos son indicativos de la coexistencia de cuatro modos de gobernanza a los cuales se les puede atribuir diversas intervenciones de gobernanza dirigidas a incidir sobre el comportamiento del caficultor. Parece existir un predominio de intervenciones atribuidas a la auto-gobernanza, que resalta la participación del mercado; y una tendencia orientada a la adopción de prácticas agrícolas intensivas. Los resultados revelan la complejidad de interacciones de condiciones marco para la caficultura que pueden influir en la toma de decisiones de los caficultores. (Tomado de la fuente)Coffee productive landscapes (CPP) constitute a source of food, cultural identity, social life, aesthetic value and rural life. However, the complex dynamics in which coffee farming is immersed have generated great pressure on PPC, which compromise the potential provision of other nature's contributions to people (NCP) and undermine the natural foundations of coffee farming itself. The intensification of coffee growing contributes to the creation of landscapes with a high supply of coffee at the expense of other NCP (trade-off), while other productive alternatives can favor the provision of various contributions. These productive alternatives are made viable, among other ways, through governance since the latter can guide, through governance interventions, agricultural management decisions towards certain production models. With the purpose of contributing to the understanding of the impact of governance modes on agricultural production practices and, therefore, on the potential provision of other NCP in a coffee socio-ecological system (SES), this thesis proposed, through qualitative research, analyze the typology of governance and its impact on the practices and NCP. For this, it became necessary to determine the modes of governance, the agricultural practices adopted, the nature's contributions to people that are perceived and the frame conditions for coffee growing that influence adoption decisions. The findings are indicative of the coexistence of four modes of governance to which various governance interventions aimed at influencing coffee grower behavior can be attributed. There seems to be a predominance of interventions attributed to self-governance, which highlights market participation; and a trend towards the adoption of intensive agricultural practices. Likewise, the results reveal the complexity of interactions of framework conditions for coffee growing that can influence the decision making of coffee growers.MaestríaMagíster en Medio Ambiente y DesarrolloLa presente tesis es un investigación cualitativa de corte descriptivo.Medio Ambiente.Sede Medellí
Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe
No full textThe main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) com-pared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs)for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. with-out failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.sponsorship: This work is part of the research supported by the projects: SustES -Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797); IGA AF MENDELU No. TP 7/2015 with the support of the Specific University Research Grant, provided by the Ministry of Education, Youth and Sports of the Czech Republic; the I4S Project within the BMBF BonaRes Program (031B0513I); RPR was supported by the German Federal Ministry of Education and Research (BMBF) via the BARISTA project (031B0811A) and via SALLnet (01LL1304A); Scientific support of climate change adaptation in agriculture and mitigation of soil degradation (ITMS2014+ 313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF'; MRR and AR were supported by Spanish National Institute for Agricultural and Food Research and Technology and Agencia Estatal de Investigacion; -Grant MACSUR02 -APCIN2016-0005-00-00; -RF, CD, DV, LG and MM acknowledge financial support from MACSUR-2 knowledge hub funded for the Italian partnership by the Ministry of Agricultural, Food and Forestry Policies (D.M. 24064/7303/15 of 16/Nov/2015); SUSTAg project (INIA, 652915 ERA-NET co-funded by FACCE-SURPLUS); the Comunidad de Madrid (Spain) and structural funds 2014-2020 (ERDF and ESF); - project AGRISOST-CM S2018/BAA-4330; Spanish MINECO AgroScena-UP (PID2019-107972RB-I00). (SustES|CZ.02.1.01/0.0/0.0/16_019/0000797, Specific University Research Grant, by the Ministry of Education, Youth and Sports of the Czech Republic|TP 7/2015, I4S Project within the BMBF BonaRes Program|031B0513I, German Federal Ministry of Education and Research (BMBF) via the BARISTA project|031B0811A, German Federal Ministry of Education and Research (BMBF) via SALLnet|01LL1304A, Integrated Infrastructure Operational Programme - ERDF|ITMS2014+ 313011W580, Spanish National Institute for Agricultural and Food Research and Technology, Agencia Estatal de Investigacion|MACSUR02 -APCIN2016-0005-00-00, MACSUR-2 knowledge hub|24064/7303/15, SUSTAg project (INIA)|652915, Comunidad de Madrid (Spain), structural fund 2014-2020 (ERDF), structural fund 2014-2020 ( ESF), Spanish MINECO AgroScena-UP|PID2019-107972RB-I00, AGRISOST-CM S2018/BAA-4330)status: Published onlin
