Miquel Ninyerola

A METHODOLOGICAL APPROACH OF CLIMATOLOGICAL MODELLING OF AIR TEMPERATURE AND PRECIPITATION THROUGH GIS TECHNIQUES

This study proposes an empirical methodology for modelling and mapping the air temperature (mean maximum, mean and mean minimum) and total precipitation, all of which are monthly and annual, using geographical information systems (GIS) techniques. The method can be seen as an alternative to classical interpolation techniques when spatial information is available. The geographical area used to develop and apply this model is Catalonia (32000 km 2 , northeast Spain). We have developed a multiple regression analysis between these meteorological variables as the dependent ones, and some geographical variables (altitude (ALT), latitude (LAT), continentality (CON), solar radiation (RAD) and a cloudiness factor (CLO)) as the independent ones. Data for the dependent variables were obtained from meteorological stations, and data for the independent variables were elaborated from a 180 m resolution digital elevation model (DEM). Multiple regression coefficients (bn) were used to build final maps, using digital layers for each independent variable, and applying basic GIS techniques. The results are very satisfactory in the case of mean air temperature and mean minimum air temperature, with coefficients of determination (R 2 ) between 0.79 and 0.97, depending on the month; in the case of mean maximum air temperature, R 2 ranges between 0.70 and 0.89, while in the case of precipitation, it ranges between 0.60 and 0.91. Copyright

Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought

Climate change is progressively increasing severe drought events in the Northern Hemisphere, causing regional tree die-off events and contributing to the global reduction of the carbon sink efficiency of forests. There is a critical lack of integrated community-wide assessments of drought-induced responses in forests at the macroecological scale, including defoliation, mortality, and food web responses. Here we report a generalized increase in crown defoliation in southern European forests occurring during 1987–2007. Forest tree species have consistently and significantly altered their crown leaf structures, with increased percentages of defoliation in the drier parts of their distributions in response to increased water deficit. We assessed the demographic responses of trees associated with increased defoliation in southern European forests, specifically in the Iberian Peninsula region. We found that defoliation trends are paralleled by significant increases in tree mortality rates in drier areas that are related to tree density and temperature effects. Furthermore, we show that severe drought impacts are associated with sudden changes in insect and fungal defoliation dynamics, creating long-term disruptive effects of drought on food webs. Our results reveal a complex geographical mosaic of species-specific responses to climate change–driven drought pressures on the Iberian Peninsula, with an overwhelmingly predominant trend toward increased drought damage.

Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

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.

Rural abandoned landscapes and bird assemblages: winners and losers in the rewilding of a marginal mountain area (NW Spain)

Abstract In many regions of Europe, large-scale socio-economic changes have led to the abandonment of rural activities and a gradual takeover of natural vegetation. It is important to assess the relative positive and negative effects of land abandonment on particular areas where the low-intensity farming is no longer socially or economically viable in order to quantify the potential conservation costs and benefits of a rewilding as a land-use management policy. During the period 2000–2010, we studied the land-use/land-cover changes in an abandoned mountain landscape (Galicia, NW Spain) and evaluated the effects on breeding bird occurrence and distribution. For this purpose, we analysed remotely sensed data-derived maps in combination with data obtained from bird censuses carried out in 2000 and 2010 at both landscape and census plot scale. The results revealed a gradient of change from bare ground and open shrubland to closed shrubland and woodland. Thirteen shrubland and forest bird species showed a significant increase (including species of conservation concern such as Turtle Dove, Dartford Warbler and Western Bonelli’s Warbler), while four ecotone and open-habitat species (e.g. Red-backed Shrike) showed a significant negative trend. In conclusion, rewilding appears to have overall positive effects on biodiversity and should be considered by policy makers as alternative land-use strategy in marginal mountain areas, particularly if they have been historically affected by wildfires. Fire management aimed at favouring the creation of small burned areas in progressively closed landscapes derived from rewilding may be a complementary alternative to maintain open habitats in these areas.

Relationship between projected changes in future climatic suitability and demographic and functional traits of forest tree species in Spain

The response of plant species to future climate conditions is probably dependent on their ecological characteristics, including climatic niche, demographic rates and functional traits. Using forest inventory data from 27 dominant woody species in Spanish forests, we explore the relationships between species characteristics and projected changes in their average climatic suitability (occurrence of suitable climatic conditions for a species in a given territory) obtained by empirical niche-based models, under a business-as-usual climate change scenario (A1, HadCM3, 2001–2100). We hypothesize that most species will suffer a decline in climatic suitability, with a less severe for species (i) currently living in more arid climates or exhibiting a broader current climatic niche; (ii) with higher current growth rates; (iii) with functional traits related to resistance to water deficits. The analysis confirm our hypothesis since apart from a few Mediterranean species, most species decrease their climatic suitability in the region under future climate, characterized by increased aridity. Also, species living in warmer locations or under a wider range of climatic conditions tend to experience less decrease in climatic suitability. As hypothesized, a positive relationship was detected between current relative growth rates and increase in future climatic suitability. Nevertheless, current tree mortality did not correlate with changes in future climatic suitability. In contrast with our hypothesis, functional traits did not show a clear relationship with changes in climate suitability; instead species often presented idiosyncratic responses that, in some cases, could reflect past management. These results suggest that the extrapolation of species performance to future climatic scenarios based on current patterns of dominance is constrained by factors other than species autoecology, particularly human activity.

Improving Air Temperature Modelization by Means of Remote Sensing Variables

In this article we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve instantaneous, mean, maximum and minimum air temperatures for daily, monthly and annual periods between 2000 and 2005 on a regional scale (Catalonia, North-West Spain) by means of multiple regression analysis and spatial interpolation techniques. Best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged test R2=0.67 and averaged RMS error=1.22degC for daily temperatures and averaged test R2=0.90 and averaged RMS error =0.84degC for monthly and annual temperatures.

Developing spatially and thematically detailed backdated maps for land cover studies

ABSTRACT Global or regional land cover change on a decadal time scale can be studied at a high level of detail using the availability of remote sensing data such as that provided by Landsat. However, there are three main technical challenges in this goal. First, the generation of land cover maps without reference data is problematic (backdating). Second, it is important to maintain high accuracies in land cover change map products, requiring a reasonably rich legend within each map. Third, a high level of automation is necessary to aid the management of large volumes of data. This paper describes a robust methodology for processing time series of satellite data over large spatial areas. The methodology includes a retrospective analysis used for the generation of training and test data for historical periods lacking reference information. This methodology was developed in the context of research on global change in the Iberian Peninsula. In this study we selected two scenes covering geographic regions that are representative of the Iberian Peninsula. For each scene, we present the results of two classifications (1985–1989 and 2000–2004 quinquennia), each with a legend of 13 categories. An overall accuracy of over 92% was obtained for all 4 maps.

Historical citizen science to understand and predict climate-driven trout decline

Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence–absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.

Estimating actual evapotranspiration by means of remote sensing data and sap flow measurements in Pinus sylvestris forest stands in a mediterranean mountain region

The aim of this study is to monitor daily actual evapotranspiration (AETd) in Pinus sylvestris forest stands using a simplified methodology. To compute AETd we have followed the beta-method, which is based on the energy budget, using a series of TERRA/AQUA MODIS and Landsat-5 TM as Remote Sensing data from 2003 to 2004. We have compared Remote Sensing AETd estimates with sap flux measurements obtaining a RMSE of 0.78 mm in the case of Landsat data, 0.93 mm in the case of TERRA MODIS and 0.91 mm in the case of TERRA AQUA using a variable B parameter in AETd modeling.