Improving Sediment Transport Modelling By A Combination Of Field Data And Sensitivity Analysis

Traditionally, the relationship between flow rate and sediment concentration in rivers has been estimated empirically. However, the problem of empirical data collection is that it is often difficult to cover the entire range of flow rates. Especially higher flow rates are often undersampled, which might lead to an underestimation of modelled sediment transport as flood events are often associated with important erosion events. In order to overcome this limitation, the introduction of the transport capacity concept can establish a safe upper bound of this sediment transport relationship. Nevertheless, given the number of implied variables in this equation, there is a high uncertainty associated with it. In this study, we aim to reduce the uncertainty on the modelled sediment transport by constraining the relation between sediment concentration and flow rate by means of a combination of sensitivity analysis and field data. This theory is implemented in a model that was used to simulate sediment transport in the Guadalquivir river basin, one of the most important rivers in the Mediterranean (56 978 km2). The sediment concentration-flow rate relation was established by combining the empirical data for the lower flow domain and Yang’s total load formula for the upper flow domain. In combination with data from automated gauging networks, the total annual sediment transport was calculated to be between 6.0 106 and 13.1 107 Mg year-1. A global sensitivity analysis of the main parameters of Yang’s equation was done to identify key data input constraints. This revealed that one of the most important parameters was the mean sediment diameter. A field sampling of flood deposits was done inmediately after high flow events to determine its range

Impact of Climate Change on Agricultural Droughts in Spain

Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using novel static and dynamic drought indices. Statistically downscaled climate change scenarios from the model HadGEM2-CC, under the scenario representative concentration pathway 8.5 (RCP8.5), were used at a total of 374 sites for the period 2006 to 2100. The evolution of static and dynamic drought stress indices over time show clearly how drought frequency, duration and intensity increase over time. Values of static and dynamic drought indices increase over time, with more frequent occurrences of maximum index values equal to 1, especially towards the end of the century (2071–2100). Spatially, the increase occurs over almost the entire area, except in the more humid northern Spain, and in areas that are already dry at present, which are located in southeast Spain and in the Ebro valley. This study confirms the potential of static and dynamic indices for monitoring and prediction of drought stress

Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools)

Multiscale fatigue modelling of additively manufactured metallic components

Additively manufactured metallic components have been used in medical and aerospace applications. In these components, surface roughness and porosity are integral features that might significantly reduce their fatigue lives, especially in the high cycle fatigue regime. Thus, to precisely estimate the fatigue life of an additively manufactured component, these defective features are incorporated into our proposed fatigue model. To capture the local plasticity caused by the defects, a nonlinear isotropic-kinematic hardening elasto-plasticity model is employed in our finite element (FE) models. Additionally, the gas-entrapped pores are modeled as circles whilst the surface topography, which was measured using stylus-based profilometer, is explicitly mo deled in the FE models. The finite element results are post-processed by our in-house software to extract the Smith-Watson-Topper (SWT) fatigue indicator parameter. This parameter is calculated at each element centroid of the FE mesh, i.e., the local indicator. Afterward, an average value of the SWT parameter over a so-called critical area whose center is located at the considered centroid is also calculated, i.e., the nonlocal indicator. The results show that the local SWT indicator is too conservative in predicting the fatigue life of the componentwhile the nonlocal SWT one can provide good results

A Farmer’s Perspective on the Relevance of Grassland-Related Innovations in Mediterranean Dehesa Systems

Grasslands are of key importance for the provision of ecosystem services (ES). Suitable management is essential to guarantee their persistence and functionality. There is a growing interest in innovations such as new technologies aimed at facilitating and improving the management of grasslands while increasing their provision of ES. The uptake of innovations by farmers is a complex process, and relevant socio-economic or technological factors that are crucial to farmers are often overlooked. This information can be useful for increasing the adoption of these innovations through the design of public policies to facilitate them. This paper analyses the relevance of the main innovations that can be applied to the management of the grasslands of Dehesa farms for the farmers and the factors that might affect this relevance. Through questionaries, we gathered information on the relevance that farmers give to the selected innovations and analysed it by cumulative link models. The results show that innovations aimed at increasing the biomass production of grasslands and resilience such as the use of seed mixtures and the use of forage drought-resistant species are considered highly relevant by Dehesa farmers. However, high-tech innovations such as GPS collars were poorly rated which could denote low applicability to the context of Dehesas or the existence of barriers hindering the adoption but also a need for further development and better information on their potential. Characteristics of the farmer and farm such as age, education level, and stocking rate seem to be related to the relevance given to some of the innovations. These results provide insightful information for the implementation and research of relevant grassland-related innovations in the context of Mediterranean Dehesa/Montado systems, as well as for the design of policies supporting them

A Virtual Lab Environment For Improving Students’ Understanding Of Infiltration And Runoff Processes

Infiltration of water in the soil is a key process in the hydrological cycle and its understanding is paramount for students of any course related to Hydrology. Different equations exist, which, in function of boundary conditions and the level of detail required in any particular case can become complex to solve in an analytical way. Water movement in the soil can be described by the Richards equation, but its solution requires numerical methods. Therefore, amongst the most used ways of calculating water infiltration are the Green and Ampt method, Horton’s equation, Philip’s equation and the Curve Number method. In all these equations, different soil parameters intervene that can be measured in the field or estimated from soil properties. The understanding of the physical meaning of these parameters, their relative importance and of the differences between the abovementioned methods is important for students. Infiltration measurements in the field or lab are time-consuming. We therefore developed a tool that helps the students to calculate infiltration in a step-by-step way, and which helps to easily change the input parameter values so as to understand the importance of the different variables. This virtual lab environment was coded in Matlab and used for the first time during the academic year 2013-2014. In this study, we present the main characteristics of this didactic software tool and present an evaluation of its usefulness by students

Determination of agro-environmental zones in Spain and sensitivity to global climatic change

Póster presentado en la Conferencia EGU 2013, 07-12 Abril 2013, Viena, AustriaSoil has a key role in the regulation of carbon, water and nutrient cycles. Traditionally, agricultural soil management was oriented towards optimizing productivity. Nowadays, mitigation of climate change effects and maintaining long-term soil quality are evenly important. Developing policy guidelines for best management practices need to be site-specific, given the large spatial variability of environmental conditions within the EU. Therefore, it is necessary to classify the different farming zones that are susceptible to soil degradation. Especially in Mediterranean areas, this variability and its susceptibility to degradation is higher than in other areas of the EU. The objective of this study is therefore to delineate current agro-environmental zones in Spain and to determine the effect of global climate change on this classification in the future. The final objective is to assist policy makers in scenario analysis with respect to soil conservation. Our classification scheme is based on soil, topography and climate (seasonal temperature and rainfall) variables. We calculated slope and elevation based on a SRTM-derived DEM, soil texture was extracted from the European Soil Database and seasonal mean, minimum and maximum precipitation and temperature data were gridded from publically available weather station data (Aemet). Global change scenarios are average downscaled ensemble predictions for the emission scenarios A2 and B2. The k-means method was used for classification of the 10 km x 10 km gridded variables. Using the before-mentioned input variables, the optimal number of agro-environmental zones we obtained is 8. The classification corresponds well with the observed distribution of farming typologies in Spain. The advantage of this method is that it is a simple, objective method which uses only readily available, public data. As such, its extrapolation to other countries of the EU is straightforward. Finally, it presents a tool for policy makers to assess the impact of global change on farming systems and to plan soil conservation measures