Afforesting arid land with renewable electricity and desalination to mitigate climate change

Land area for afforestation with RE-based SWRO desalination

Restoration land³⁷ and bare land areas²² with the following water stress conditions were determined to be areas where forests could grow if irrigated with a secure water supply. The projected water stress, water supply and demand data for the decade 2040 are used. The renewable water resources in these areas were not considered sufficient to sustain forest growth.

• Land nodes that lie in high (40% < WS <80%) and extremely high water stress basins (WS > 80%)

• Land nodes that lie in low water stress basins (WS < 10%) but have low water supply (<10 cm)

• Land nodes that lie in arid and low water use basins (water demand < 3 cm and water supply < 10 cm)

Restoration land potential accounts for several environmental variables of soil, climate and topographic layers. The corresponding tree canopy covers for all the restoration nodes (Supplementary Fig. 1) were set to be the maximum area of the nodes that can be afforested.

Bare land data were obtained from the 2009 land cover map²². The maximum canopy cover assumed for bare land nodes was 20%, which is an estimate of the global average of the restoration data. In this data set, environmental variables suitable for forest cover were not considered. This may impact the tree canopy cover possible on these lands. Nevertheless, if there is a secure water supply, the tree canopy cover on the bare lands could be higher than 20% and warrants further research. Reflecting this approach, Egypt is a country where the reclamation of desert land has been carried out with expectations to convert more than 7% of desert to arable land, but this effort has been hindered by the lack of water resources⁴⁸. In this research, the intention is to use available bare land within a limit, given reliable water supplies.

The tree mix chosen for this study is suitable for desert or tropical conditions. Thus, the restoration areas and bare land areas with afforestation potential but with inconducive climates were excluded. The suitable areas were extracted by considering the corresponding climatic zone, the US Department of Agriculture hardiness zone and the subsequent temperature range that suits all trees. The suitable temperature range was determined to be all places where the temperature does not drop below 5 °C for more than 5 days (Supplementary Fig. 2, Table 1 and Note 1).

Supplementary Fig. 3 presents an outline of the overall concept of this study. Supplementary Fig. 4 is a flowchart illustrating the overall approach taken in this study and provides a framework for the methods outlined below.

Plotting CO₂ sequestration of trees

The following trees were chosen on the basis of data from refs. ^23,49 and the Centre for Urban Forest Research (CUFR) tree carbon calculator:

1. 1.

   Coolibah tree

2. 2.

   Paper bark

3. 3.

   Date palm

4. 4.

   Turkish pine

5. 5.

   Willow acacia

6. 6.

   Iron wood

7. 7.

   Kamani

8. 8.

   White mulberry.

The allometry equations to define all trees, except date palms, were verified from the Urban Tree Database⁴⁹. It has to be noted that in the case of high-density forests, the allometry equations may differ from those of urban trees, which would have sufficient space to grow. However, given the availability of data, the urban growth equations were used as in Supplementary Data 1. The CO₂ sequestration rates were based on the data in the CUFR calculator for the tree species when grown in tropical or desert climate zones (Supplementary Data 1). The data from the CUFR calculator provide the aboveground and belowground biomass stored for the individual tree types. The carbon pools, soil, litter and dead wood were accounted for on the basis of estimates from the literature (Supplementary Data 1). It has to be noted that urban trees grow under good conditions and are maintained as opposed to forests⁴⁹. In this research, the trees are intended to be irrigated and maintained regularly to ensure the productivity and self-sustainability of the forests.

The Urban Tree Database⁵⁰ explains that there are gaps in the data for date palms due to the lack of measurements. Thus, the corresponding aboveground and belowground biomass values were obtained from ref. ⁵¹, which measured and developed allometric equations for date palm species on arid land.

In a high-density, multispecies forest, trees grown will be healthier and have a higher carbon sequestration potential⁵⁰. To emulate forest sequestration potentials, the ratio of the trees of each type in a unit area was varied such that the maximum annual carbon stored (tC ha⁻¹ yr⁻¹) is similar to that of a mature tropical forest. A tropical forest was assumed since such forests would generally not have a lack of renewable water resources. The validation of the carbon sequestration data was carried out in the respective worksheet of Supplementary Data 1 using the share of the 8 tree species specified. Mature trees were considered to be 40 yr in this study, while the range found in the literature could be 20–70 yr.

Equations (1) and (2) illustrate the method to determine the cumulative carbon sequestered over time for each land node with afforestation potential and the corresponding total country value:

where Node_area is the total afforestation area available in the node^21,22,37, TreeShare_type is the percentage share of the tree type from the total mix of trees, TreeCarbon_type,yr^{49,50,51,52,53,54,55} is the cumulative carbon sequestered by the tree type by a specific year and total aff. nodes is the total number of land nodes with afforestation available in the country. TreeCarbon_type,yr was obtained from the data in Supplementary Data 1 where the cumulative CO₂ stored over the 70 yr period is presented for all 8 tree species. The data on the shares of the tree types are also presented in Supplementary Data 1.

Water demand of the tree mix

The water demand for the tree types are a function of the reference ET_o of the location, the tree species’ corresponding water use coefficient in the region type and the efficiency of the irrigation equipment used²³. The global ET_o data were obtained from the FAO Map Catalog⁵⁶, the data being the average values from the time period 1961–1990 in 0.16° × 0.16° resolution. The water use coefficients are specific to the tree species and region type, and is a share of the ET_o (Supplementary Fig. 5). Equations (3–5) present the method used to determine the water demand for every afforestation node in a year, and the corresponding water demand for the country.

where WaterDemand_type,yr is the water demand of an individual tree type in a specific year, Coefficient_type is the tree species’ specific water-use coefficient, Eto_node is the reference evapotranspiration rate for the afforestation node, IrrigEfficiency is the efficiency of the sub-surface drip irrigation (95% in this study), AreaIrrigated is the maximum share of the afforestation area that is irrigated (90% in this study), Final_TotalWater_node,yr is the final water demand taking into account precipitation, RecyclyingShare(yr) is the share of the evapotranspiration that is precipitated and is dependent on the year, CountryWater_yr is the total water demand for the afforestation area in the country and year.

The irrigation equipment used are the high-efficiency sub-surface drip irrigation systems that have been used in Oman to irrigate date palm plantations⁵⁷ and in Texas for crops such as corn, cotton and soybean⁵⁸. The details of the irrigation system used are provided in Supplementary Data 1. The increase in water demand is proportional to the canopy area of the trees^23,59. Once the total canopy area of the trees in an afforestation node is equal to the maximum afforestation area of the node, the water demand is set to not increase. The increasing canopy cover also results in an increase in precipitation, which has been estimated and accounted for as recycling in Supplementary Fig. 6 (refs. ^25,26). This is applied to the water demand obtained in equation (3) to account for precipitation recycling over time as shown in equation (4). The competition for local water resources from local vegetation, which may increase the final water demand, is not considered. The use of highly efficient sub-surface drip irrigation systems to deliver water directly to the trees for the full period of 70 yr, coupled with the possible increase in precipitation as the trees mature, is intended to ensure that the water demand of the trees is always met.

LUT Energy System Transition Model

The LUT-ESTM^18,19 has been used extensively to analyse energy system transition pathways towards entirely RE-based energy systems on a global, regional and country basis. In this study, a simplified version of the model¹⁵ is used to cost-optimize the energy system for the SWRO desalination plants necessary for afforestation for the time period 2030–2100. The optimization is carried out in an hourly temporal resolution and a 0.5° × 0.5° spatial resolution. Supplementary Figs. 14 and 15 illustrate the LUT-ESTM set up and flow of steps in this study, respectively. The electricity is generated by solar PV and wind power plants on the basis of the RE resources. Battery storage and power-to-gas components are used to complement the electricity generation sources, ensuring cost-optimal operation of the SWRO plants.

The financial and technical parameters of all energy system components used in this research up to 2050 are varied according to specific data sources and presented in ref. ⁶⁰. These parameters include the capital costs, operating costs, lifetime and efficiency values. However, due to the lack of projection data after 2050, the parameters of all components are kept the same as at 2050. Similarly, the parameters for the SWRO desalination system, water pumping and piping are obtained from ref. ¹⁵ where all relevant references are presented. On the basis of the lifetime of all system components, the components are decommissioned and replaced during the period 2030–2100. It has to be noted that the fixed operating costs for the SWRO desalination plants account for maintenance and replacement of membranes, assuming an annual replacement rate of 15%. The SWRO desalination plants, pumps and pipes are decommissioned on the basis of their respective lifetimes.

The results of the model were used to calculate the LCOE, LCOW and CO₂ sequestration costs for all nodes with afforestation potential during the 70 yr period. Final results on a country basis are presented in Supplementary Data 2. The CO₂ costs also accounted for the land rent, conversion costs, monitoring costs, operation and maintenance (O&M) costs and fertilizer costs as shown in Supplementary Data 1 and 2. These costs were obtained from ref. ⁴ where data are provided in a regional format. The capital and operating costs of sub-surface drip irrigation equipment and the decommissioning costs during the period 2030–2100 were also accounted for.

The model opts to run the SWRO desalination plants at high full load hours to minimize the costs⁶¹. This means that due to the variable nature of solar and wind resources, there are higher RE capacities installed than required for some hours of the year, as it is cheaper to curtail the energy than increase storage. For the modelled system, this results in a global average electricity excess of about 24% of the total demand. However, in a more efficient sector-coupled energy system, this excess would be used by other sectors to their advantage^18,19. As such, an excess limit of 10% was specified. The excess electricity was considered to be sold to the other sectors at the LCOE of the energy system for that year, and the income accounted for in the final system LCOE (Supplementary Fig. 12 and observable in further detail in Supplementary Data 2).

The land use of the ground-mounted single-axis tracking PV, the fixed-tilted PV and onshore wind power plant capacities modelled in this study were determined using current and projected power densities. The projections accounted for improvements in the efficiency of the technologies. The power density values and corresponding land use data are shown in Table 2 and Supplementary Fig. 17, respectively, with more details in Supplementary Data 2.

Annual historic CO₂ cost

The annual historic CO₂ cost is the annualized cost of running the energy, desalination, irrigation and land for that year and the annual average CO₂ sequestration rate.

where all annualized costs are in billion euros for the year and average CO₂ sequestration rate is in GtCO₂ yr⁻¹ for the specific node. All costs for the countries modelled can be found in Supplementary Data 2. The financial and technical parameters for the desalination and energy system can be found in ref. ⁶⁰, the irrigation system details are provided in Supplementary Data 1 and the land costs are provided in Supplementary Fig. 13. As explained in the Discussion, a WACC of 5% was used for all countries.

The annualized costs of the energy system, desalination system, irrigation and land systems for a specific node and year are described in equations (7–10), respectively.

where ‘et’ represents each of the energy system components, Etech is the total number of energy system components, Capex_et is the capital expenditure of each energy system component, crf_et is the capital recovery factor for each energy system component, opexfix_et is the fixed operational expenditure of each energy system component, Cap_et is the operating capacity of each energy system component, Egen_et is the electricity generation of each energy system component and opexvar_et is the variable operational expenditure of each energy system component.

where dt represents each of the desalination system components (SWRO desalination plants and water storage), Dtech is the total number of desalination system components, Cap_dt includes the annual water production from SWRO desalination and water storage for the node, vp are the vertical pumping components, Water_node is the annual water transported to node, hp are the horizontal pumping components, Vertical_node is the average elevation for the node from the nearest coastline and Horizontal_node is the horizontal water pumping distance for the node from the nearest coastline.

where ‘it’ represents the sub-surface drip irrigation system, Area_node,yr is the afforestation area for each node

where Conversion_node,yr is the land conversion cost in the specific node, LandRent_node,yr is the annual land rent for the specific node, Monitoring_node,yr is the monitoring cost for the forests, O&M_node,yr is the operation and maintenance cost and Fertilizer_node,yr is an annual fertilizer cost. Equation (11) is used to determine the capital recovery factor for all system components.

where WACC is the weighted average cost of capital, t is the system component and Nt is the lifetime of the system component.

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