Improved process descriptions in the PESERA model

Table of Contents

1. Authors
2. Abstract
3. Introduction
4. General extensions to PESERA
   1. Grazing
   2. Fire
   3. Wind Erosion
5. Particular extensions to PESERA
   1. Mulching and/or maintaining ground cover vegetation within tree crops
   2. Retention of crop residues as litter layer at harvesting of arable and other crops
   3. Irrigation and water harvesting for croplands
   4. Invasion and clearance of unpalatable species
   5. Terracing and strip cropping
   6. Nitrogen budgeting and rotations
6. Other extensions to PESERA
   1. Mass movements
   2. Data collection calibration of PESERA against reservoir data
7. Conclusions
8. References

Fire

We have implemented a simplified fire model within PESERA, using simplified versions of algorithms developed and tested independently (Venevsky et al, 2002) for Portugal. A fire danger index (FDI) is calculated as:

where α=0.00037

and

where are respectively the mean monthly temperature and temperature range, and D_>3 is the number of days in the month with more than 3 mm of rain.

The number of wild fire start-ups depends on two factors, the number of lightning strikes (0.1 to 10 per km² per year) and the number of visitors. The former is the dominant factor in the Sahel and the latter in southern Europe. The probability of a fire is then calculated as the number of start-ups multiplied by the Fire Danger Index. Once started, the area of a wild fire is calculated from the rate of spread, which decreases with the fuel load (dry vegetation biomass) and increases with the wind speed. Within the PESERA model the fire area cannot exceed one complete grid cell (normally 1 km²), which is adequate for all but the most catastrophic fires, which will commonly be represented by fire start-ups in many adjacent cells.

In establishing the equilibrium state, fire is ignored. However, for a time series, there are options to include random fires (drawn at random with the calculated fire probability) and managed fires (regularly applied in a selected month of the year). These fires are assumed to destroy a fixed fraction of the vegetation biomass over the fire area, reducing the biomass in the grid cell, with knock-on effects to runoff and erosion in subsequent years. We propose to further calibrate this model in association with the Swansea and Aveiro partners working in Portugal, and extend these methods for managed fire behaviour. Fire should also affect soil properties, and there may be scope to do this through pedo-transfer functions, but there is no experimental data to support parameterisation for this at present.

This work was begun for the DeSurvey project (20%) but has largely been brought to fruition within DESIRE, where it is ongoing, involving collaboration with Portuguese partners, particularly to improve parameterisation with respect to intensity of fires and soil responses.