*2.2.1 Climate layers and future scenarios*

The assessment was conducted based on five climatological zones of Malawi (**Figure 2**). Seasonal climatic characteristics and other information about the five climatological zones in Malawi are presented in **Table 1**. The assessment used three scenarios: Near-century; mid-century; and end-century with the following time frames: 2011–2040; 2041–2070; and 2071–2100, respectively. The projected precipitations and temperatures for the three scenarios were obtained from the Ministry of Forestry and Natural Resources in the Department of Climatic Change and Meteorological Services

**Figure 2.** *Malawi's five climatological zones.*


#### **Table 1.**

*The area and seasonal climatic characteristics of climatological zones in Malawi.*

(DCCMS), Blantyre, Malawi. Briefly, projections for future precipitation and temperature were developed using the 20 global scale general circulation models (GCMs). These are downscaled outputs used in the Intergovernmental Panel on Climate Change Fifth Assessment report. The GCMs were used in concurrence with two representative concentration pathways (RCPs; RCP4.5 and RCP8.5) [12]. Observed

*Impact of Climate Variability on Forest Vegetation Zones in Malawi DOI: http://dx.doi.org/10.5772/intechopen.106850*

**Figure 3.**

*The Holdridge Life Zone model concept framework that divides the world territorial ecosystems into 39 vegetation zones.*

data temperatures and precipitation data used was for the period 1961–2010 while daily temperatures and precipitation data used was for 1971–2000.

#### *2.2.2 Holdridge life zone model*

Holdridge life zone (HLZ) model was used to assess climate change impact on forest type while QGIS3.2 was used to produce the forest type maps. HLZ model is well explained by Li et al. [6]. Briefly, The HLZ model is a classic climate- vegetation model designed by Holdridge [13]. It divides world territorial ecosystems into 39 vegetation zones (**Figure 3**). The 39 vegetation zones are mapped in a triangular coordinate system with three key climatic variables [6, 13]. The three key climatic variables are: annual bio temperature (ABT), annual precipitation (AP), and potential evapotranspiration ratio (PER) [6]. In this study ABT, AP and PER were estimated using the following equations [6]:

$$ABT = \frac{1}{12} \sum\_{i=1}^{12} T\_i \tag{1}$$

$$AP = \sum\_{i=1}^{12} P\_i \tag{2}$$

$$PER = 59.93 \,\text{x} \frac{ART}{AP} \tag{3}$$

Where *T*i is monthly mean temperature and *P*i is monthly precipitation. In addition, QGIS 3.2 was also used to analyze the change area of the vegetation zone.

#### *2.2.3 GAP formind modified model and biomass estimation*

The impact of climate variability on forest living biomass was assessed using the GAP-Formind modified model, while the above and below ground biomass were estimated using the following site-specific models [14].

$$AGB = 0.21691 \ge DBH^{2.31891} \tag{4}$$

$$BGB = 0.284615 \ge DBH^{1.99268} \tag{5}$$

Where AGB and BGB are above and below ground biomass (kg dry matter per tree), respectively and DBH is the diameter at breast height (cm). The total living biomass per tree was estimated by adding up AGB to BGB. In Addition, the following National Forest Inventory (NFI) data for different climatological zones were used for the estimation of biomass:

