Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Accueil du site → Doctorat → Canada → Climate data and climate-based seed zones for Mexico : guiding reforestation under observed and projected climate change

University of Alberta (2019)

Climate data and climate-based seed zones for Mexico : guiding reforestation under observed and projected climate change

Castellanos Acuna, Dante

Titre : Climate data and climate-based seed zones for Mexico : guiding reforestation under observed and projected climate change

Auteur : Castellanos Acuna, Dante

Université de soutenance : University of Alberta

Grade : Doctor of Philosophy (PhD) 2019

Résumé partiel
Seed zones for forest tree species have been used for decades to guide seed movement in reforestation programs, ensuring that seedlings are well adapted to their planting environments. Seeds may be collected and planted anywhere within a zone, but not across zone boundaries. The zones are geographic delineations that often track ecosystem boundaries, and comprise areas of similar climate and other environmental conditions. However, under climate change, this management approach is no longer valid. Local seed sources become increasingly lagged behind the environments to which they are optimally adapted. Here, I develop a climate-based seed zone system for Mexico to address observed and projected climate change. For climate-based geographical delineations, I develop an interpolated climate database for past conditions (1901-2015) as well as for future projections for the 2020s, 2050s. and 2080s. While high quality interpolated climate data for temperature variables are widely available, existing datasets for precipitation have a number of shortcomings. Precipitation patterns in complex terrain, such as orographic lift effects and rain shadows, are generally difficult to model, and high quality products are only available for some regions of the world, namely for the United States, western Canada, and Europe. To address this issue for Mexico and other parts of the world where high quality precipitation grids are not available, I start with the compilation of precipitation weather station records from nine open-access databases (CRU, GHCN, FAO, WMO, ECA, R-HydroNet and USFS). The database was cross-checked for errors, duplicates were removed, location and elevation errors corrected, and missing precipitation values were estimated where possible. The database was then spatially subsampled, retaining only the most reliable records with a balanced spatial and elevational distribution, targeting one station per 40km grid cell and per 100m elevation interval. The resulting database contained 45,888 stations from an original 98,631 stations, excluding duplicates. This represents an approximately 50% larger compilation than any of the original databases, even after spatial subsampling. Subsequently, I developed a new interpolation approach that models monthly long-term precipitation patterns for the 1961-1990 normal period, based on weather station data, wind measurements, and topographical exposure. The model was implemented through a local, universal kriging approach that uses wind speed, wind direction, as well as topographic aspect and slope to build an exposure covariate

Mots clés : Precipitation climatological data Climate-based seed zones Historical and projected future climate data

Présentation

Version intégrale

Page publiée le 13 avril 2021