Present and future climate conditions for winegrowing in Spain

This study deals with the question of how winegrowing in Spain may be altered by anthropogenic climate change. The present state and expected future development of three bioclimatic indices relevant for winegrowing were assessed by observation, and four regional climate models from the EU-ENSEMBLES project were investigated. When comparing the 2061–2090 scenario period to the 1961–1990 reference period, the models unanimously indicate a significant increase in the mean of the two considered thermal indices over the entire study region. However, for the index based on temperature and precipitation, the models are heavily biased when verified against observations and generally disagree on the size of the projected future change. For this index, unanimous model agreement was only found for northwestern Spain where all models indicated a significant decrease in the mean. From these results, regional climate change is expected to negatively affect the quality of wine in the growing regions of central and southern Spain, and the Ebro valley, whereas positive effects should be expected in the northwest. No significant changes in the risk of mildew infestation are to be expected except for the northwest, where this risk is projected to decrease.     

The impact of pesticides on oxidative stress level in human organism and their activity as an endocrine disruptor

Pesticides cause serious environmental and health problems both to humans and animals. The aim of this review is to discuss selected herbicides and fungicides regarding their mode of action and their influence on basic oxidative stress parameters and endocrine disruption properties tested in selected cell cultures in vitro. Because of numerous difficulties which animal studies are subject to, cell cultures are an excellent experimental model reflecting human exposure to different pesticides through all relevant routes. This experimental model can be used to monitor aggregate and cumulative pesticide exposures.     

Water quality index for agricultural systems in Northwest Uruguay

Agricultural systems have experienced rapid expansion and intensification in the last several decades. In Uruguay, since the beginning of 2000, the most common cropping systems have included soybeans. Currently, this crop is expanding towards lowlands traditionally occupied by rice in rotation with pastures. However, the environmental effects of agricultural intensification and diversification are not well known. Thus, some indices have been proposed to quantify the changes in agricultural production systems and assess water quality. The main goal of this study was to develop a water quality index (WQI) to assess the impacts of the diversification of rice production systems in northwest Uruguay. The study was carried out in an agricultural basin where other summer crops have been incorporated in the rice-pasture sequence. Agriculture intensification and crop diversification indices were calculated using information provided by farmers. Water samples were collected downstream of the production area before crop sowing and after crop harvest (2008–2009 to 2010–2011 and 2016–2017 to 2017–2018). Biochemical oxygen demand, nitrates, total phosphorus, fecal coliforms, and total suspended solids were the variables that mainly explained the effects of the agricultural activities on water quality. The proposed water quality index included these unweighted variables, which allowed for the pre-sowing and post-harvest to be differentiated, as well as the degree of diversification. Therefore, the proposed WQI constitutes a tool that can be used to evaluate the water quality in an agricultural basin. Likewise, it can be used to select agricultural sequences that generate the least possible impacts on the associated water resources.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Macro- and microelement distribution in organs of Glyceria maxima and biomonitoring applications

The content of nutrients (N, P, K, Ca and Mg) and of trace metals (Fe, Cu, Mn, Zn, Pb, Cd, Co and Ni) in water, bottom sediments and various organs of Glyceria maxima from 19 study sites selected in the Jeziorka River was determined. In general, the concentrations of nutrients recorded in the plant material decreased in the following order: leaf>root>rhizome>stem, while the concentrations of the trace elements showed the following accumulation scheme: root>rhizome>leaf>stem. The bioaccumulation and transfer factors for nutrients were significantly higher than for trace metals. G. maxima from agricultural fields was characterised by the highest P and K concentrations in leaves, and plants from forested land contained high Zn and Ni amounts. However, the manna grass from small localities showed high accumulation of Ca, Mg and Mn. Positive significant correlations between Fe, Cu, Zn, Cd, Co and Ni concentrations in water or sediments and their concentrations in plant indicate that G. maxima may be employed as a biomonitor of trace element contamination. Moreover, a high degree of similarity was noted between self-organizing feature map (SOFM)-grouped sites of comparable quantities of elements in the water and sediments and sites where G. maxima had a corresponding content of the same elements in its leaves. Therefore, SOFM could be recommended in analysing ecological conditions of the environment from the perspective of nutrients and trace element content in different plant species and their surroundings.     

Building resilience to water scarcity in southern Spain: a case study of rice farming in Doñana protected wetlands

Agricultural water management needs to evolve in view of increased water scarcity, especially when farming and natural protected areas are closely linked. In the study site of Doñana (southern Spain), water is shared by rice producers and a world heritage biodiversity ecosystem. Our aim is to contribute to defining adaptation strategies that may build resilience to increasing water scarcity and minimize water conflicts among agricultural and natural systems. The analytical framework links a participatory process with quantitative methods to prioritize the adaptation options. Bottom-up proposed adaptation measures are evaluated by a multi-criteria analysis (MCA) that includes both socioeconomic criteria and criteria of the ecosystem services affected by the adaptation options. Criteria weights are estimated by three different methods—analytic hierarchy process, Likert scale and equal weights—that are then compared. Finally, scores from an MCA are input into an optimization model used to determine the optimal land-use distribution in order to maximize utility and land-use diversification according to different scenarios of funds and water availability. While our results show a spectrum of perceptions of priorities among stakeholders, there is one overriding theme that is to define a way to restore part of the rice fields to natural wetlands. These results hold true under the current climate scenario and even more so under an increased water scarcity scenario.