A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques

By Miquel Ninyerola, Xavier Pons, J. Roure. 2000

611 citations recorded by the source corpus.

Subfields: Atmospheric Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/93ccfe48dedcb0a3679cec9e687f9c7070f6116d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Water Science and Technology

Citations in the corpus

Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale (2007, methodology): “Rather then using the UK with a trend function modelled via a set of covariates, some authors (e.g. Agnew and Palutikof, 2000; Ninyerola et al., 2000; Antonic et al., 2001) have proposed a simpler approach based on RK, i.”
Plant guilds drive biomass response to global warming and water availability in subalpine grassland (2006, methodology): “Climatic conditions in the two locations were obtained from the Atlas Climatic Digital de Catalunya ( Ninyerola, Pons & Roure 2002 ).”
Exploring species attributes and site characteristics to assess plant invasions in Spain (2009, background): “Spain houses a heterogeneous climatic mosaic with three main climatic types: oceanic Mediterranean, continental and Mediterranean maritime (Ninyerola et al ., 2000).”
Post‐fire recovery of Mediterranean ground ant communities follows vegetation and dryness gradients (2006, methodology): “Eight vegetation types (Table 1) exposed to fires in 1994 were selected using the Mapa Forestal de Catalunya (1 : 100,000) for the period 1996–99.”
MeteoInfo: GIS software for meteorological data visualization and analysis (2014, background): “The powerful mapping capability of GIS is a primary factor that makes it attractive to the atmospheric community (Ninyerola et al ., 2000; Dyras and Seraﬁn-Rek, 2005; Shipley, 2005).”
A statistical method based on remote sensing for the estimation of air temperature in China (2015, background): “The use of auxiliary data (e.g. distance to the ocean, longitude, and latitude) can help to alleviate this problem and substantially increase the estimation accuracy (Kurtzman and Kadmon, 1999; Ninyerola et al., 2000).”
Presence-absence versus presence-only modelling methods for predicting bird habitat suitability (2004, methodology): “We also used climatic variables (temperature, precipitation and solar radiation) which were obtained from the Catalan Digital Atlas (CDA, Ninyerola et al. 2000), whereas data on topography was obtained from a Digital Elevation Model (DEM) generated by the ICC from topographic 1:50 000 maps.”
Risk analysis of potential invasive plants in Spain (2010, background): “…Spain houses a heterogeneous climatic mosaic being oceanic in the North, alpine at high altitudes and somehow continental in the central plateau (but far less extreme than the Central Europe continental climate) most of its territory is influenced by Mediterranean climate (Ninyerola et al. 2000).”
Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs (2015, methodology): “Many disciplines (biogeography, hydrology, forest management, agriculture, ecology and others) use spatial information about climatological data as a basis for understanding the processes they study (Ninyerola et al., 2000).”
Modelling the effects of irrigation schemes on the distribution of steppe birds in Mediterranean farmland (2004, methodology): “Environmental variables (ENV) were generated from environmental information available as GIS layers.”
Determinants of distribution of six Pinus species in Catalonia, Spain (2001, methodology): “Environmental variables A climate model (Ninyerola et al. 2000) was used to generate layers of monthly precipitation and monthly minimum and maximum temperatures for the entire study area.”
Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses (2009, methodology): “The climatic coverages of the period 1999–2005 (means, minimum, maximum, and range of temperature, air moisture, and rainfall) were computed according to the interpolation techniques described by Ninyerola and Pons (Ninyerola et al. 2000) using climatic records of the Andalusian Agroclimate Informat”
Patterns in intraspecific variation in root traits are species‐specific along an elevation gradient (2020, methodology): “Mean annual temperature and MAP were modelled and mapped at 1 km 2 resolution using GIS (Ninyerola et al., 2000) based on spatially distributed variables that characterise the geography, topography (altitude, slope, exposure, distance to the sea) and land-use (Bertrand et al., 2011; Piedallu et…”