Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham e-Theses
You are in:

Modelling surface climate over complex terrain for landscape ecology

Joyce, Andrew Noel (2000) Modelling surface climate over complex terrain for landscape ecology. Doctoral thesis, Durham University.

[img]
Preview
PDF
7Mb

Abstract

Climate exerts a fundamental control on ecosystem function, species diversity and distribution. Topographic variability may influence surface climate, through processes operating at a landscape- scale. To quantify and model such influences, the topography of a 72 km(^2) area of complex terrain, (including the Moor House National Nature Reserve in northern England) was analysed at 50 m resolution. A suite of topographic variables was created, including distance relative to the Pennine ridge (dist), and elevation difference between each grid cell and the lowest grid cell within a specified neighbourhood {drain). Automatic weather stations (AWS) were deployed in a series of networks to test hypothetical relationships between landscape and climate. Daily maximum air temperature, daily mean soil temperature and daily potential evapotranspiration can be modelled spatially using a daily lapse rate calculated from the difference between daily observations made at two base stations. On days with a south easterly wind direction, daily mean temperature is estimated as a function of lapse rate and dist; the spatial behaviour of temperature is consistent with a föhn mechanism. Daily minimum temperature is modelled using lapse rate and drain on days with a lapse rate of minimum temperature shallower than -2.03 x 10 C m(^-1), incorporating the effects of katabatic air flow. Daily solar radiation surfaces are estimated by a GIS routine that models interactions between slope and solar geometry and accounts for daily variations in cloudiness and daylight duration. The daily climate surfaces were tested using data measured at a range of AWS locations during different times of year. The accuracy of the daily surfaces is not seasonally-dependent. The spatial climate data are particularly well suited to landscape-scale ecology because the methods account for prevailing topoclimatic constraints and because separate climate surfaces are generated for each day, capturing the high frequency variability characteristic of upland regions. [brace not closed]

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Thesis Date:2000
Copyright:Copyright of this thesis is held by the author
Deposited On:01 Aug 2012 11:43

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitter