Dinámica del agua edáfica en dehesas y su relación con el clima y la vegetación

  1. Lozano Parra, Javier
Supervised by:
  1. Susanne Schnabel Director

Defence university: Universidad de Extremadura

Fecha de defensa: 20 February 2015

Committee:
  1. Gerardo Moreno Marcos Chair
  2. Loes Van Schaik Secretary
  3. Joaquín Francisco Labado Contador Committee member
  4. Antonio Ceballos Barbancho Committee member
  5. Francesc Gallart Gallego Committee member

Type: Thesis

Teseo: 377398 DIALNET

Abstract

This work investigates the soil moisture dynamic at different spatio-temporal scales in agrosilvopastoral ecosystems (�dehesa�s), conditioned by the availability of water. The relationships between such dynamics and the factors involved, such as climate and vegetation, are also analyzed, as well as the response and sensibility of the pasture to soil water availability. Soil water content was monitored continuously with a temporal resolution of 30 minutes by means of capacitance sensors, along of two hydrological years. They were installed at 5, 10 and 15 cm, and another depth depending on soil profile. Soil temperature was monitored at 5 cm each 30 minutes. Sensors were gathered in 17 soil moisture stations in two contrasting situations: under tree canopy and open spaces. Temporal scales ranged from minute to decades. Spatial scales varied from soil profile to catchment. These works were conducted in 3 farms in Extremadura, Spain. The results indicated that under tree cover lower annual soil water content was observed than in grasslands. The importance of the upper soil layer (first 15 cm) on ecohydrological processes and as the main layer to supply water for pasture production was highlighted. The influence of dry and wet periods on soil moisture was also highlighted, as well as on interception processes. Soil wetting patterns were defined and quantified. �Multivariate Adaptive Regression Spline� proved being useful tool to identify the factors influencing flow types and modelling their occurrence. The combined use of ecohydrological spatially distributed models and stochastic weather generators proved to be an effective tool.