Search

The study area is Urlaţi wine growing centre - Dealul Mare vineyard (Prahova County, Romania). The aim of the research is the assessment of viticultural potential and delineation of natural terroir units by using a new GIS based multicriteria methodology that includes environmental criteria specific to Romanian viticulture. The viticultural potential revealed by the methodology is expressed as types of wines that can be produced in a given area in accordance with its ecological suitability. The spatial distribution of the environmental parameters and the computation of their joint influence were achieved by using GIS. The results show that Urlaţi wine-growing centre has the viticultural potential to produce red and white quality wines. The wine production directions revealed by the research are similarl to those traditionally practised in Urlaţi wine growing centre that validates the methodology. The map of viticultural potential reveals the existence of three distinct natural terroir units: a natural terroir unit suitable for red quality wines production (73.16% from the assessed area); a natural terroir unit suitable for white quality wines and red table wines production (25.41% from the assessed area); a natural terroir unit suitable exclusively for white quality wines production (1.42% from the assessed area).

Thermal regime of Cotnari vineyard, Iaşi County, Romania, was surveyed between 2011-2012 using 10 Tinytag temperature loggers TK-4014, spread in 10 different lands of the vineyard. Research is developed in the framework of the international project TERVICLIM (France), by means of was created a global network of analyses points of vineyard climate, spread in 13 countries from both hemispheres of the globe, at different latitudes. The data recorded by temperature loggers in Cotnari vineyard shows that in 2011-2012 winter, the minimum temperatures dropped to -28.6ºC, with differences of up to 7.1ºC between the coldest and the mildest lands of the vineyard. Spatial distribution of the most harmful frost (12 February 2012) shows that more than 80% from the vineyard area was exposed to temperatures lower than -24ºC, 17% to harmful temperatures between -21..- 24ºC and 3% to temperatures between -18..- 21ºC, less damaging to vines. As consequences, the damages in the vine plantations varied from less significant negative consequences for vines in the mildest location, up to massive losses of winter buds, damages of annual branches and frozen vines in the coldest land of the vineyard. Analytical data of winter buds losses spatial distribution shows that at - 28.6ºC (absolute minimum temperature for the vineyard), 0.46% (7.29 ha) of vine plantations were exposed to vines losses, 3.81% of plantations to losses of annual branches, 62.49% from vine plantations to winter buds losses exceeding 40%, difficult to compensate by pruning, 13.5% to losses of winter buds exceeding 60%, requiring reestablishing the vegetative potential of vines. The only vine plantations protected by harmful frosts were those located at over 175 m altitude, in the highest lands of the vineyards., Regimul termic al podgoriei Cotnari a fost monitorizat în perioada 2011-2012 cu ajutorul a 10 senzori termici Tinytag TK- 4014, amplasaţi în 10 locaţii diferite din podgorie. Cercetarea se desfăşoară în cadrul proiectului internaţional TERVICLIM (Univ. Rennes 2, Franţa), prin care s-a creat o reţea de monitorizare a climatului arealelor viticole cu puncte de observaţie în 20 de podgorii din 13 ţări, situate la diferite latitudini, în ambele emisfere ale globului. Datele înregistrate de senzorii termici Tinytag în podgoria Cotnari arată că, în iarna 2011-2012, temperaturile minime negative au coborât până la -28.6ºC (12 februarie 2012), cu diferenţe de până la 7.1ºC între cel mai rece şi cel mai puţin rece plai al podgoriei. Din analiza distribuţiei spaţiale a gerului din data de 12 februarie 2012 rezultă că mai mult de 80% din plantaţiile viticole ale podgoriei au fost expuse la temperaturi mai mici de -24ºC (dăunătoare pentru lemnul multianual), 17% la temperaturi de -21…-24ºC (dăunătoare pentru ochii de iarnă şi lemnul anual) şi 3% la temperaturi de -18..-21ºC (mai puţin dăunătoare pentru viţa de vie). Ca urmare, daunele produse plantaţiilor viticole au variat de la nesemnificative pe plaiurile cu climat blând, până la pierderi masive de ochi de iarnă, distrugerea prin îngheţ a coardelor anuale şi chiar distrugerea prin îngheţ a butucilor de viţă de vie. Datele analitice privind distribuţia spaţială a daunelor provocate viţei de vie arată că, la emperatura de -28.6ºC (minima absolută din iarna 2011-2012), 0.46% (7.29 ha) din suprafaţa podgoriei a fost expusă distrugerii butucilor de viţă de vie, 3.81% distrugerii coardelor anuale, 62.49% pierderilor de muguri mai mari de 40%, dificil de compensat prin tăieri, 13.5% pierderilor de muguri care depăşesc 60% şi care impun refacerea butucilor de viţă de vie. Singurele plantaţii ferite de îngheţurile nocive din iarna 2011-2012 au fost cele situate la mai mult de 175 m altitudine.

International audience; Landslide susceptibility constitutes an important component within the landslide risk equation and therefore, itsassessment is very useful for improving the prognosis of such phenomena. The present research aims to assess landslide susceptibilityfor two low hills regions from Romania, situated in different geographical conditions. The application of the logisticregression method aimed to reveal the relations among the factors controlling the landslides’ manifestation and their spatialdistribution. The computer aided data processing in GIS environment points out the slope and land use as the most importantcausal factors for landsliding, followed by terrain aspect, distance from drainage network and precipitations. Landslide susceptibilitymaps were achieved for both study regions. The validity of the prediction model was checked using the ROC curve(Receiver Operating Characteristic) and the AUC (Area Under the Curve) respectively, which reveal a high accuracy degree.; Cet article propose l’évaluation de la susceptibilité aux glissements de terrain dans deux secteurs de collinesbasses de Roumanie. Par l’application de la méthode de régression logistique, on se propose de saisir les relations entreles facteurs responsables de l’occurence des glissements de terrain et leur distribution spatiale. Le traitement automatiquedes données par SIG prend en compte la pente, la nature géologique du terrain, l’exposition des versants, ladistance par rapport au réseau de drainage et les précipitations annuelles. On obtient ainsi deux cartes de susceptibilitéaux glissements. La validité du modèle de prédiction a été vérifié à l’aide de la courbe ROC (Receiver Operating Characteristic),respectivement pour l’aire AUC située sous la courbe (Area Under the Curve), en mettant en évidence un degréélevé de prédiction.Cet article propose l’évaluation de la susceptibilité aux glissements de terrain dans deux secteurs de collinesbasses de Roumanie. Par l’application de la méthode de régression logistique, on se propose de saisir les relations entreles facteurs responsables de l’occurence des glissements de terrain et leur distribution spatiale. Le traitement automatiquedes données par SIG prend en compte la pente, la nature géologique du terrain, l’exposition des versants, ladistance par rapport au réseau de drainage et les précipitations annuelles. On obtient ainsi deux cartes de susceptibilitéaux glissements. La validité du modèle de prédiction a été vérifié à l’aide de la courbe ROC (Receiver Operating Characteristic),respectivement pour l’aire AUC située sous la courbe (Area Under the Curve), en mettant en évidence un degréélevé de prédiction.

Any transformation of a discrete variable into a continuous one is subject to uncertainty. Consequently the identification and assessment of errors is essential for avoiding misinterpretations of models describing the spatial distribution of climatic parameters. Our study attempts to identify the main sources of errors affecting the statistical spatial models of climatic parameters and to assess their impact on the accuracy of these models: e.g. errors from data or metadata, models, or results. Imprecision and uncertainty must be evaluated in the spatial modeling process because it seems essential to propose results taking them into account (the uncertainty of the outcome has to be estimated). Managing uncertainty is essential as it plays an increasing significant role in decision-making. In particular, we discuss the georeference errors, the errors derived from the heterogeneity of the studied region, from the spatial representativeness of the station network, the outliers problem, the statistical errors and the propagation of errors from elementary to complex climatic variables.

The creation of temperature fields, in contrasted terrain, must be founded on a model taking into account characteristics and spatial evolutions of the ground. For this reason, multivariate regression often gets the best results, compared to geostatistical spatial interpolation. The parameters used as independent variables in this multivariate regression must be correlated to air temperature. These factors' influences vary according to spatial and temporal studied scales: thus, if at annual scale, elevation is often the only element correlated with air temperature, at daily scale, the model is more complex. We propose here to highlight some topographic parameters derived from geographical information systems, as well as statistical methods (low-pass filters in particular) generating an improvement of the correlation coefficients obtained with air temperature. Some examples dealing with mountainous area (northern part of the French Alps and Moldova, in Romania) are presented., La création de champs de température, en relief contrasté, doit être fondée sur un modèle qui prenne en compte les caractéristiques et les évolutions spatiales du terrain. A ce titre, une modélisation par régression multiple procure souvent les meilleurs résultats, par rapport aux méthodes d'interpolation spatiale. Les paramètres utilisés comme variables indépendantes dans cette régression multiple doivent bien sûr être corrélés à la température. L'influence de ces facteurs varie suivant les échelles spatiales et temporelles de l'étude : ainsi, si à l'échelle annuelle, l'altitude est souvent le seul élément influençant les températures, il n'en est rien à l'échelle quotidienne. Nous proposons ici de mettre en évidence quelques paramètres topographiques pouvant être modélisés par systèmes d'information géographique, ainsi que des méthodes statistiques (filtres passe-bas notamment) engendrant une amélioration des coefficients de corrélation obtenus avec la température. Le travail sera illustré par des exemples ayant pour cadre des régions essentiellement montagneuses (partie nord des Alpes françaises et région de Moldavie, en Roumanie).