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The latest projections indicate that sea‐level rise (SLR) is certain to exceed 2 m in coming centuries, and a rise by 4 m is considered possible. Radar‐based land elevation models applied to date suggest that the increase of area below mean sea level, that is potentially exposed to permanent flooding, will accelerate as SLR proceeds, being relatively limited initially. However, applying new and more accurate satellite LiDAR elevation data we find the opposite pattern, with the fastest increase in the area of exposed land occurring in the early stages of SLR. In one‐third of countries most of this increase will be caused by the first meter of SLR and in nearly all within the first 2 m. We conclude that in many regions the time available to prepare for increased exposure to flooding may be considerably less than assumed to date, and that better elevation data will support timely preparations. The global LiDAR lowland DTM (GLL_DTM_v2) elevation data set developed for this assessment is available in the public domain. Plain Language Summary: The latest sea‐level rise (SLR) projections indicate that future sea levels are certain to exceed 2 m and a rise by 4 m is considered possible. Land elevation models applied to date suggest that the increase of land area below sea level will be limited at first but will go faster when SLR continues. When we apply a new and more accurate elevation model we find the opposite pattern, with the fastest increase during the early stages of SLR. In one‐third of countries most of this increase will be during the first meter of SLR, and in almost all within the first 2 m. We conclude that in many regions the time available to prepare for increased exposure to flooding may be considerably less than assumed to date, and that better elevation data will support timely preparations. The global LiDAR lowland DTM (GLL_DTM_v2) elevation data set developed for this assessment is available in the public domain. Key Points: Calculations using radar‐based global elevation models available to date have generally underestimated the extent of lowest coastal areas that are most exposed to sea‐level rise (SLR)A recent lowland elevation model (global LiDAR lowland DTM (GLL_DTM_v2)) derived from only satellite LiDAR data is currently most accurate, we recommend such LiDAR data to be used in SLR impact assessmentsApplying this model we find that the greatest increase in coastal area below mean sea level will occur in the early stages of SLR, contrary to earlier assessments [ABSTRACT FROM AUTHOR]

We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C·ha-1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR-albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires. [ABSTRACT FROM AUTHOR]

Tropical peatland fires play a significant role in the context of global warming through emissions of substantial amounts of greenhouse gases. However, the state of knowledge on carbon loss from these fires is still poorly developed with few studies reporting the associated mass of peat consumed. Furthermore, spatial and temporal variations in burn depth have not been previously quantified. This study presents the first spatially explicit investigation of fire-driven tropical peat loss and its variability. An extensive airborne Light Detection and Ranging data set was used to develop a prefire peat surface modelling methodology, enabling the spatially differentiated quantification of burned area depth over the entire burned area. We observe a strong interdependence between burned area depth, fire frequency and distance to drainage canals. For the first time, we show that relative burned area depth decreases over the first four fire events and is constant thereafter. Based on our results, we revise existing peat and carbon loss estimates for recurrent fires in drained tropical peatlands. We suggest values for the dry mass of peat fuel consumed that are 206 t ha−1 for initial fires, reducing to 115 t ha−1 for second, 69 t ha−1 for third and 23 t ha−1 for successive fires, which are 58-7% of the current IPCC Tier 1 default value for all fires. In our study area, this results in carbon losses of 114, 64, 38 and 13 t C ha−1 for first to fourth fires, respectively. Furthermore, we show that with increasing proximity to drainage canals both burned area depth and the probability of recurrent fires increase and present equations explaining burned area depth as a function of distance to drainage canal. This improved knowledge enables a more accurate approach to emissions accounting and will support IPCC Tier 2 reporting of fire emissions. [ABSTRACT FROM AUTHOR]

Drought analysis needs continuous long time-series of monthly rainfall. Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) which determine meteorological and hydrological drought index respectively are computed with the input data from TRMM 3B42RT and APHRODITE. The results are compared to the ground station data, in order to determine which data has good performance to describe drought condition in Pemali-Comal river basin. The meteorological drought index computed from TRMM gives better result than APHRODITE for SPI of 3 to 12 months, except for SPI 1 month. Nevertheless, the hydrological drought index from TRMM generally has better result than APHRODITE. [ABSTRACT FROM AUTHOR]

Unlike meteorological drought, hydrological drought in Indonesia has not been routinely monitored. This paper discusses hydrological drought monitoring and prediction system in Pemali-Comal River Basin by utilizing a Drought Early Warning System (DEWS) based on Delft-FEWS software. The Standardized Runoff Index (SRI) is applied to river discharges data which is collected through a real-time telemetering system. In case of unavailability of river discharge data, TRMM satellite rainfall data is used to simulate river discharges. A preliminary prototype of DEWS shows that the characteristics of previous drought events can be evaluated, and the simulation shows the possibility of forecasting hydrological drought with time lag of 6 months. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Water management in lowland areas usually aims to keep water tables within a narrow range to avoid flooding and drought conditions. A common water management target parameter is the depth of the canal water table below the surrounding soil surface. We demonstrated a method that rapidly determines canal water table depth (CWD) from airborne LiDAR data. The water table elevation was measured as the minimum value determined in a grid of 100 m × 100 m applied to a 1 m × 1 m digital terrain model (DTM), and the soil surface was calculated as the median value of values in each grid cell. Results for areas in eastern Sumatra and West Kalimantan, Indonesia, were validated against 145 field measurements at the time of LiDAR data collection. LiDAR-derived CWD was found to be accurate within 0.25 m and 0.5 m for 86% and 99% of field measurements, respectively, with an R2 value of 0.74. We demonstrated the method for CWD conditions in a drained peatland area in Central Kalimantan, where we found CWD in the dry season of 2011 to be generally below −1.5 and often below −2.5 m indicating severely overdrained conditions. We concluded that airborne LiDAR can provide an efficient and rapid mapping tool of CWD at the time of LiDAR data collection, which can be cost-effective especially where LiDAR data or derived DTMs are already available. The method can be applied to any LiDAR-based DTM that represents a flat landscape that has open water bodies. [ABSTRACT FROM AUTHOR]

• Natural regeneration of peatswamp forest on degraded peatlands is driven by distance to forest, mammal and bird dispersal. • Regeneration occurred in clusters of young trees at distances up to 2 km from the forest. • Forest burnt in 1997 had higher species diversity and trees per plot than intact forest. • Natural regeneration could restore degraded peatlands surrounding remnant forest patches up to 500 m from natural forest. Restoration of peat swamp forest (PSF) on degraded Southeast Asian peatlands could reduce global carbon emissions and biodiversity loss. However, multiple ecological barriers are believed to hinder natural regeneration of native trees on degraded peatland and make restoration expensive. We evaluated if natural PSF regeneration occurs and what factors may influence it on eight different land use and land cover (LULC) classes with different types of disturbance, including drainage and fire, in a retired Acacia crassicarpa Benth. (Acacia) plantation landscape. The study involved 42 plots inside five PSF LULCs – intact, logged, burnt (1997, 2015), remnant and 212 plots at distances up to 2 km from the PSF edge in three Acacia plantation LULCs – unharvested, harvested, and burnt. The number of species per plot were similar between intact PSF (25 ± 6 (SD) per 20 m × 10 m plot), logged forest (30 ± 6) and 1997 burnt forest (30 ± 13) but lower in 2015 burnt forest (11 ± 10) and remnant forest (18 ± 11). Regeneration away from the PSF across all degraded LULCs varied from fern dominated areas with no regeneration to clusters with high stem densities. The plantation LULCs, unharvested (94 species) and harvested Acacia (71 species), had similar overall species diversity after 3–4 years of regeneration to the intact and logged PSF (90 species). In unharvested Acacia , total species diversity, species per plot and stem density decreased with distance from forest edge (1–300 m – 87 species; 9 ± 6 (SD) species per 20 m × 10 m plot; 1,056 stems/ha; 301–500 m – 33; 5 ± 2; 511 and >500 m – 38; 6 ± 3; 683). In harvested Acacia , there was low plot species diversity irrespective of distance from the forest (1–300 m – 51; 4 ± 2; 578; 301–500 m – 17; 4 ± 2; 1,100; >500 m – 48; 4 ± 2; 780). Factors which may influence regeneration differed between different LULCs, but there was a clear influence of distance from forest edge and dispersal mechanism – i.e. whether a tree was bird or mammal dispersed and the interaction between these two factors. While our study suggests that if not further disturbed by logging, drainage and/or fire, degraded PSF could regenerate naturally to a similar species diversity as intact PSF, the lower levels of natural regeneration further away from the forest may warrant selective planting of species which do not disperse over long distances. More study is needed on the factors facilitating natural regeneration, whether it leads to restoration of PSF ecosystem functioning and the role of Acacia as a potential regeneration catalyst. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]