Comparació de models que prediuen les respostes de la vegetació ibèrica al canvi global

Les prediccions de la resposta de la vegetació ibèrica al canvi global es basen en dos tipus de models, uns de base matemàtica (models fisiològics) que reprodueixen el funcionament de les plantes i uns altres de base estadística (models correlatius) que es basen en establir correlacions amb les condicions ambientals actuals.Un estudi mostra les congruències i incongruències entre aquests dos tipus de models i la importància de les interaccions entre organismes. També destaca la importància de comparar diversos models per entendre millor els factors que condicionen la distribució de la vegetació.Las predicciones de la respuesta de la vegetación ibérica al cambio global se basan en dos tipos de modelos, unos de base matemática (modelos fisiológicos) que reproducen el funcionamiento de las plantas y otros de base estadística (modelos correlativos) que se basan en establecer correlaciones con las condiciones ambientales actuales. Un estudio muestra las congruencias e incongruencias entre estos dos tipos de modelos y la importancia de las interacciones entre organismos. También destaca la importancia de comparar diversos modelos para entender mejor los factores que condicionan la distribución de la vegetación

Cartografía de usos y cubiertas del suelo del sureste de la Península Ibérica a partir de la clasificación de imágenes Landsat en el quinquenio 2000-2004

Se presenta la metodología utilizada en la obtención de la cartografía de usos y cubiertas del suelo para la zona oriental de Andalucía (ámbito de 29.259 km²) en el quinquenio 2000-2004, empleando el clasificador híbrido y utilizando imágenes Landsat junto con las variables auxiliares. Las áreas de entrenamiento se han obtenido de manera semiautomática a partir del SIOSE y depuradas con umbrales de NDVI. Se han ejecutado bancos de pruebas en función de las variables incluidas en el clasificador. Los mejores resultados, en cuanto a porcentaje de acierto y área clasificada,se han obtenido excluyendo la radiación solar de invierno y la banda 1. El proceso se ha realizado por separado para cubiertas naturales y urbanas, con un grado de acierto global superior al 88%, y para cultivos, con un acierto superior al 86%

Modeling air temperature through a combination of remote sensing and GIS data

Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002-2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000-2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat-5 (TM), Landsat-7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R2 = 0.60 and averaged root mean square error (RMSE) = 1.75C for daily temperatures, and averaged R2 = 0.86 and averaged RMSE = 1.00C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11% and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling

Phenological sensitivity and seasonal variability explain climate-driven trends in Mediterranean butterflies

Although climate-driven phenological shifts have been documented for many taxa across the globe, we still lack knowledge of the consequences they have on populations. Here, we used a comprehensive database comprising 553 populations of 51 species of north-western Mediterranean butterflies to investigate the relationship between phenology and population trends in a 26-year period. Phenological trends and sensitivity to climate, along with various species traits, were used to predict abundance trends. Key ecological traits accounted for a general decline of more than half of the species, most of which, surprisingly, did not change their phenology under a climate warming scenario. However, this was related to the regional cooling in a short temporal window that includes late winter and early spring, during which most species concentrate their development. Finally, we demonstrate that phenological sensitivity-but not phenological trends-predicted population trends, and argue that species that best adjust their phenology to inter-annual climate variability are more likely to maintain a synchronization with trophic resources, thereby mitigating possible negative effects of climate change. Our results reflect the importance of assessing not only species' trends over time but also species' abilities to respond to a changing climate based on their sensitivity to temperature

Does the gap-filling method influence long-term (1950-2019) temperature and precipitation trend analyses?

Incomplete climatic series require gap-filling approaches so they can be used in homogeneous long-term spatiotemporal trend analyses. Monthly mean Temperature (MT) and Precipitation (PR) databases from the meteorological stations of the Iberian Peninsula have a high percentage of data gaps: 80.21% and 73.25% for the period 1950-1979 (P1), and 61.82% and 58.03% for the period 1980-2019 (P2). The different gap-filling methods of the Emmentalsoftware were tested to determine their performance and whether the gap-filling method influences these trend analyses. The nonparametric Theil-Sen approach and the Mann-Kendall test were used to assess the trend magnitude and its significance. The results showed (i) similar patterns between the evaluated methods, but with (ii) spatial differences, especially during P1. (iii) The comparison between standardized gap-filled and unfilled series did not show significant differences for MT and PR, although a reduction in the trend variability occurred in the first case (filled). (iv) Summer mean temperatures showed the largest warming trend (0.27 °C/decade), while autumn showed the smallest (0.21°C/decade) (median data for P1 and P2). Overall, an increase of 1.45 °C occurred in the entire period (annual median). (v) PR did not show any clear trend in any month in the entire period. This research has shown how climate trends can be affected by a reduction in data variability due to the application of gap filling methods. Although accounting for variability is of crucial importance for climate analysis, ignoring discontinuities in derived climatic surfaces causes greater spatiotemporal inconsistencies in derived climate products

Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales

Improving mean minimum and maximum month-to-month air temperature surfaces using satellite-derived land surface temperature

Month-to-month air temperature (T) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month T mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical T modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum T, but also for maximum T. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum T (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data

Environmental and socioeconomic factors of abandonment of rainfed and irrigated crops in northeast Spain

Changes over the last century in the economic model of European countries and the development of the market economy have led to intense shifts in land occupation patterns. Agricultural abandonment is an important consequence of these processes and has modified natural and cultural landscapes, involving side-effects for society. Understanding how environmental and socio-economic factors influence the abandonment process can provide useful insights for managing investments, whether from the public or the private sector. In Spain, the Pyrenees and the Ebro Depression are two differentiated areas in terms of land-use dynamics, particularly in terms of the agricultural model carried out. In this paper we have analyzed the agricultural abandonment in these areas during the 1987-2012 period in relation to several potential explanatory factors. The analysis focuses on the abandonment of rainfed and irrigated herbaceous crops in order to derive specific explanations according to the crop type and geographical region. Crop covers were classified from four Landsat scenes, and conditions were described by topographic variables, human factors and drought occurrence. Boosted regression trees (BRT) were used to identify the most important variables and to describe the relationships between agricultural abandonment and key factors. Topography derived variables were found to be the main determinants, except for irrigated crops in the Ebro Basin, where locational factors play a more important role. BRT models allowed us to identify other significant patterns such as: the vulnerability of irrigated crops to drought; the higher dependence of agricultural activity in the Pyrenees on internal networks; pattern shifts of land abandonment in the analyzed sub-periods, and; evidence of the importance of economic diversification for maintaining cropland

Driving Forces of Forest Expansion Dynamics across the Iberian Peninsula (1987-2017) : A Spatio-Temporal Transect

This study analyzes the spatio-temporal dynamics of the drivers of forest expansion in the Iberian Peninsula for the periods 1987-2002-2017 through a 185 km-wide north-south Landsat scene transect. The analysis has considered a variety of biogeographical regions [0-3500 m.a.s.l, annual rainfalls 150-2200 mm] and 30 explanatory variables. A rigorous map production at 30 m resolution, including detailed filtering methods and uncertainty management at pixel scale, provided high-quality land cover maps. The main forest expansion trajectories were related to explanatory variables using boosted regression trees. Proximity to previous forests was a key common factor for forest encroachment in all forest types, with other factors being distance to the hydrographic network, temperature and precipitation for broadleaf deciduous forests (BDF), precipitation, temperature and solar radiation for broadleaf evergreen forests (BEF) and precipitation, distance to province capitals, and solar radiation for needleleaf evergreen forests (NEFs). Results also showed contrasting forest expansion trajectories and drivers per biogeographic region, with a high dynamism of grasslands towards new forest in the Eurosiberian and the mountainous Mediterranean regions, a high importance of croplands as land cover origin of new forest in the Mesomediterranean, and increasing importance over time of socioeconomic drivers (such as those employed in the industry sector and the utilized agricultural area) in the Supramediterranean region but the opposite pattern in the Southern Mesomediterranean. Lower precipitation rates favored new NEFs from shrublands in the Thermomediterraean region which, together with the Northern Mesomediterranean, exhibited the highest relative rates of new forests. These findings provide reliable insights to develop policies considering the ecological and social impacts of land abandonment and subsequent forest expansion