Does increased hydrochemical model complexity decrease robustness?

The aim of this study was, within a sensitivity analysis framework, to determine if additional model complexity gives a better capability to model the hydrology and nitrogen dynamics of a small Mediterranean forested catchment or if the additional parameters cause over-fitting. Three nitrogen-models of varying hydrological complexity were considered. For each model, general sensitivity analysis (GSA) and Generalized Likelihood Uncertainty Estimation (GLUE) were applied, each based on 100,000 Monte Carlo simulations. The results highlighted the most complex structure as the most appropriate, providing the best representation of the non-linear patterns observed in the flow and streamwater nitrate concentrations between 1999 and 2002. Its 5% and 95% GLUE bounds, obtained considering a multi-objective approach, provide the narrowest band for streamwater nitrogen, which suggests increased model robustness, though all models exhibit periods of inconsistent good and poor fits between simulated outcomes and observed data. The results confirm the importance of the riparian zone in controlling the short-term (daily) streamwater nitrogen dynamics in this catchment but not the overall flux of nitrogen from the catchment. It was also shown that as the complexity of a hydrological model increases over-parameterisation occurs, but the converse is true for a water quality model where additional process representation leads to additional acceptable model simulations. Water quality data help constrain the hydrological representation in process-based models. Increased complexity was justifiable for modelling river-system hydrochemistry. Increased complexity was justifiable for modelling river-system hydrochemistry.

An introduction to the ‘new’ politics of agriculture and food

The agricultural policy agenda has been broadened with farm policy issues now interlinking with other policy domains (food safety, energy supplies, environmental protection, development aid, etc.). New actors promoting values which sometimes conflict, or which are not always easily reconcilable, with those previously guiding agricultural policy have entered the broader agricultural and food policy domain. The studies of various new policy issues inter-linking with the agricultural policy domain included in this special issue show that value conflicts are addressed in different ways and thus result in inter-institutional coordination and conflict unfolding differently. Studies of inter-institutional policy making in the agricultural policy sector have the potential to contribute to theoretical developments in public policy analysis in much the same way as agricultural policy studies did in the past.

Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling

Satellite-based Synthetic Aperture Radar (SAR) has proved useful for obtaining information on flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides water level observations that can be assimilated into a hydrodynamic model to decrease forecast uncertainty. With an increasing number of operational satellites with SAR capability, information on the relationship between satellite first visit and revisit times and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007,Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a large influence on forecast statistics. Revisit interval is most influential for early observations. The results are promising for the future of remote sensing-based water level observations for real-time flood forecasting in complex scenarios.

Revolutionary insurgencies, paradigmatic cases

This essay will examine and compare the production of women's writing during and after the Nicaraguan and Cuban Revolutions, a corpus that includes novels, poetry, and testimonies, but also features considerable generic hybridity. Since both revolutions are separated by some twenty years, each must be understood in the context of the specific ideological conditions that framed women’s participation in culture and revolution, and must also be considered in light of evolving gender discourses, both national and international. As such, these texts respond to both local and transnational paradigms of feminine subjectivity, and highlight the particular problems that arise from women’s insertion into the revolution and its representation in literature. These writers also have to negotiate the evolving context of revolution itself, with its moments of euphoria and disenchantment – and it is here that the greatest contrasts can be found between the two revolutionary instances and their political features: whilst Cuba’s revolutionary process has somehow survived economic crisis and created a level of political stability and continuity (not without its own problems), the Nicaraguan case is characterised by ruptures and resentments which are clearly reflected in the work of writers before, during, and after the Sandinista revolution, and which continue to the present day. This chapter will address the work of the most prominent woman writer of the Nicaraguan revolution, Gioconda Belli, whose main work reflects her involvement in the Sandinista struggle; ex-commander Mónica Baltodano has recently compiled a four volume piece with testimonials of men and women who participated in the organization and actions led by the Frente Sandinista de Liberación Nacional, and Violeta Barrios de Chamorro, former president of Nicaragua, who has recently co-authored an autobiography. There are other women poets to consider, especially under the auspices of the Asociación de Mujeres Nicaraguenses Luisa Amanda Espinoza, such as Vidaluz Meneses, Michele Najlis and Daisy Zamora. The testimonios compiled by Margaret Randall are also important pieces to consider for both Nicaragua and Cuba. The most important voices of the Cuban revolution still resident on the island are poet Nancy Morejón and writers Mirta Yáñez and Aida Bahr, with younger writers such as Adelaida Fernández de Juan and Marilyn Bobes representing a more recent generation.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Online orthographies

The representation in online environments of non-Roman-based script languages has proved problematic. During the initial years of Computer-mediated Communication, the American Standard Code for Information Interchange character set only supported Roman-alphabeted languages. The solution for speakers of languages written in non-Roman scripts was to employ unconventional writing systems, in an effort to represent their native language in online discourse. The first aim of this chapter is to present the different ways that internet users choose to transliterate or even transcribe their native languages online, using Roman characters. With technological development, and consequently the availability of various writing scripts online, internet users now have the option to either use Roman characters or their native script. If the latter is chosen, internet users still seem to deviate from conventional ways of writing, in this case, however, with regards to spelling. The second aim, therefore, is to bring into light recent developments, by looking at the ways that internet users manipulate orthography, to achieve their communicative purposes.