Forested tropical peatlands in Southeast Asia store at least 42 000 Million metric tonnes (Mt) of soil carbon. Human activity and climate change threatens the stability of this large pool, which has been decreasing rapidly over the last few decades owing to deforestation, drainage and fire. In this paper we estimate the carbon dioxide (CO₂) emissions resulting from drainage of lowland tropical peatland for agricultural and forestry development which dominates the perturbation of the carbon balance in the region. Present and future emissions from drained peatlands are quantified using data on peatland extent and peat thickness, present and projected land use, water management practices and decomposition rates. Of the 27.1 Million hectares (Mha) of peatland in Southeast Asia, 12.9 Mha had been deforested and mostly drained by 2006. This latter area is increasing rapidly because of increasing land development pressures. Carbon dioxide (CO₂) emission caused by decomposition of drained peatlands was between 355 Mt y⁻¹ and 855 Mt y⁻¹ in 2006 of which 82% came from Indonesia, largely Sumatra and Kalimantan. At a global scale, CO₂ emission from peatland drainage in Southeast Asia is contributing the equivalent of 1.3% to 3.1% of current global CO₂ emissions from the combustion of fossil fuel. If current peatland development and management practices continue, these emissions are predicted to continue for decades. This warrants inclusion of tropical peatland CO₂ emissions in global greenhouse gas emission calculations and climate mitigation policies. Uncertainties in emission calculations are discussed and research needs for improved estimates are identified.

Conversion of tropical peatlands to agriculture leads to a release of carbon from previously stable, long-term storage, resulting in land subsidence that can be a surrogate measure of CO₂ emissions to the atmosphere. We present an analysis of recent large-scale subsidence monitoring studies in Acacia and oil palm plantations on peatland in SE Asia, and compare the findings with previous studies. Subsidence in the first 5 yr after drainage was found to be 142 cm, of which 75 cm occurred in the first year. After 5 yr, the subsidence rate in both plantation types, at average water table depths of 0.7 m, remained constant at around 5 cm yr⁻¹. The results confirm that primary consolidation contributed substantially to total subsidence only in the first year after drainage, that secondary consolidation was negligible, and that the amount of compaction was also much reduced within 5 yr. Over 5 yr after drainage, 75 % of cumulative subsidence was caused by peat oxidation, and after 18 yr this was 92 %. The average rate of carbon loss over the first 5 yr was 178 t CO_2eq ha⁻¹ yr⁻¹, which reduced to 73 t CO_2eq ha⁻¹ yr⁻¹ over subsequent years, potentially resulting in an average loss of 100 t CO_2eq ha⁻¹ yr⁻¹ over 25 yr. Part of the observed range in subsidence and carbon loss values is explained by differences in water table depth, but vegetation cover and other factors such as addition of fertilizers also influence peat oxidation. A relationship with groundwater table depth shows that subsidence and carbon loss are still considerable even at the highest water levels theoretically possible in plantations. This implies that improved plantation water management will reduce these impacts by 20 % at most, relative to current conditions, and that high rates of carbon loss and land subsidence are inevitable consequences of conversion of forested tropical peatlands to other land uses.

Coastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km² or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

Sumarga, E., L. Hein, A. Hooijer, and R. Vernimmen. 2016. Hydrological and economic effects of oil palm cultivation in Indonesian peatlands. Ecology and Society 21(2):52.http://dx.doi.org/10.5751/ES-08490-210252

No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km² of land below 10, 5, and 2 m +MSL. The 0.93 million km² of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines (‘strips’) covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7–96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications.