ALBERT

Description: An analysis is carried out of the snow characteristics, distribution and disappearance on a subtropical volcano. Teide (28° 16′ N–16° 38′ W), is a stratovolcano with the highest altitude in Spanish territory at 3718 m a.s.l. It is characterized by an arid climate, with only 12.7 days of snow per year and very clear skies during most of the year. The snow cover is rarely continuous even during the cold season. In addition, the particular geothermal conditions of its ground, the layout of the lava flows from the crater, and not only its subtropical latitudinal position, are responsible for its special snow distribution and ablation processes, such as the banded and radial snow pattern, the hollows in the base layer of the snowpack and snow penitents. These features create a unique snow cover within the high mountain environments.

Description: Hail is a serious concern for agriculture on the Iberian Peninsula. Hailstorms affect crop yield and/or quality to a degree that depends on the crop species and the phenological time. In Europe, Spain is one of the countries that experience relatively high agricultural losses related to hailstorms. It is of high interest to study models that can support calculations of the probabilities of economic losses due to hail damage and of the tendency over time for such losses. Some studies developed in France and the Netherdlands show that the summer mean temperature was highly correlated with a yearly hail severity index developed from hail-related parameters obtained for insurance purposes. Meanwhile, other studies in the USA point out that a highly significant correlation between both is not possible to find due to high climatic variability. The aim of this work is to test the correlation between average minimum temperatures and hail damage intensity over the Spanish Iberian Peninsula. With this purpose, correlation analyses on both variables were performed for the 47 Spanish provinces (as individuals and single set) and for all crops and four individual crops: grapes, wheat, barley and winter grains. Suitable crop insurance data are available from 1981 until 2007 and based on this period, temperature data were obtained. This study does not confirm the results previously obtained for France and the Netherlands that relate observed hail damage to the average minimum temperature. The reason for this difference and the nature of the cases observed are discussed.

Description: Understanding and describing the spatial characteristics of soil surface microrelief are required for modelling overland flow and erosion. We employed the multifractal approach to characterize topographical point elevation data sets acquired by high resolution laser scanning for assessing the effect of simulated rainfall on microrelief decay. Three soil surfaces with different initial states or composition and rather smooth were prepared on microplots and subjected to successive events of simulated rainfall. Soil roughness was measured on a 2×2 mm2 grid, initially, i.e. before rain, and after each simulated storm, yielding a total of thirteen data sets for three rainfall sequences. The vertical microrelief component as described by the statistical index random roughness (RR) exhibited minor changes under rainfall in two out of three study cases, which was due to the imposed wet initial state constraining aggregate breakdown. The effect of cumulative rainfall on microrelief decay was also assessed by multifractal analysis performed with the box-count algorithm. Generalized dimension, Dq, spectra allowed characterization of the spatial variation of soil surface microrelief measured at the microplot scale. These Dq spectra were also sensitive to temporal changes in soil surface microrelief, so that in all the three study rain sequences, the initial soil surface and the surfaces disturbed by successive storms displayed great differences in their degree of multifractality. Therefore, Multifractal parameters best discriminate between successive soil stages under a given rain sequence. Decline of RR and multifractal parameters showed little or no association.

Description: Soil surface roughness (SSR) is an excellent indicator of soil susceptibility to wind and water erosion and plays an important role in the development and the maintenance of soil biota. Several methods have been developed to characterise SSR based on different methods of acquiring data. Because the main problems related to these methods involve the use and handling of equipment in the field, the present study aims to fill the need for a method for measuring SSR that is more reliable, low-cost and convenient in the field than traditional field methods. Shadow analysis, which interprets micro-topographic shadows, is based on the principle that there is a direct relationship between the soil surface roughness and the shadows cast by soil structures under fixed sunlight conditions. SSR was calculated with shadows analysis in the laboratory using hemispheres of different diameter with a diverse distribution of known altitudes and a surface area of 1 m2. Data obtained from the shadow analysis were compared to data obtained with the chain method and simulation of the micro-relief. The results show a relationship among the SSR calculated using the different methods. To further improve the method, shadow analysis was used to measure the SSR in a sandy clay loam field using different tillage tools (chisel, tiller and roller) and in a control of 4 m2 surface plots divided into subplots of 1 m2. The measurements were compared to the data obtained using the chain set and pin meter methods. The SSR measured was the highest when the chisel was used, followed by the tiller and the roller, and finally the control, for each of the three methods. Shadow analysis is shown to be a reliable method that does not disturb the measured surface, is easy to handle and analyse, and shortens the time involved in field operations by a factor ranging from 4 to 20 compared to well known techniques such as the chain set and pin meter methods.

Description: Soil surface roughness (SSR) expresses soil susceptibility to wind and water erosion and plays an important role in the development and the maintenance of soil biota. Several methods have been developed to characterise SSR based on different methods of acquiring data. Because the main problems related to these methods involve the use and handling of equipment in the field, the present study aims to fill the need for a method for measuring SSR that is more reliable, low-cost and convenient in the field than traditional field methods. Shadow analysis, which interprets micro-topographic shadows, is based on the principle that there is a direct relationship between the soil surface roughness and the shadows cast by soil structures under fixed sunlight conditions. SSR was calculated with shadows analysis in the laboratory using hemispheres of different diameter with a diverse distribution of known altitudes and a surface area of 1 m2. Data obtained from the shadow analysis were compared to data obtained with the chain method and simulation of the micro-relief. The results show a relationship among the SSR calculated using the different methods. To further improve the method, shadow analysis was used to measure the SSR in a sandy clay loam field using different tillage tools (chisel, tiller and roller) and in a control of 4 m2 surface plots divided into subplots of 1 m2. The measurements were compared to the data obtained using the chain set and pin meter methods. The SSR measured was the highest when the chisel was used, followed by the tiller and the roller, and finally the control, for each of the three methods. Shadow analysis is shown to be a reliable method that does not disturb the measured surface, is easy to handle and analyse, and shortens the time involved in field operations by a factor ranging from 4 to 20 compared to well known techniques such as the chain set and pin meter methods.

Description: Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example.

Description: Sea surface temperature is the key variable when tackling seasonal to decadal climate forecasts. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. In addition, recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability and investigate possible teleconnections, the sea surface temperature based statistical seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of the S4CAST model whose operation is focused on studying the impacts of sea surface temperature on any climate-related variable. Two applications focused on analyzing the predictability of different climatic events have been implemented as benchmark examples.