Climate change will affect Southeast Asian countries, particularly Thailand. There are still insufficient studies on rainfall, streamflow, and future floods in the Upper Nan Watershed, northern Thailand. This study examined how future climate change will affect the rainfall, streamflow, and flooding in the Upper Nan Watershed. SWAT and HEC-RAS models were utilized to assess the future streamflow and flooding in this area. The models used data from 1980–2020, which were taken from seven Upper Nan meteorological stations and two discharge stations. In this study, the impact of future climate change was predicted using three GCMs, under RCP4.5 and RCP8.5 scenarios. The historical data analyzed in this study indicated that rainfall in the study area has a positive trend. Climate change will increase further, from 18% to 19%, which will cause more fluctuations and lead to wetter conditions, both in the wet and dry seasons. Climate change delayed the hydrograph peak and the SWAT-modelled streamflow in the N1 and N64 stations by between 0.3% and 5.1%. RCP8.5 inundated all of the stations more than RCP4.5. Our models showed that in the medium future (2041–2060), the inundated area will be similar to that during the 100-year flood probability. Thus, monitoring and preparation are necessary to avoid repeating the considerable 2011 flood losses in Thailand.

In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, 10-, 25-, and 50-year return periods (RPs) in 2029 and 2039 in two representative concentration pathway (RCP) scenarios: the moderate climate change scenario (RCP 4.5) and the high-emission scenario (RCP 8.5). The SWAT model showed high accuracy (R² = 0.82, NSE = 0.78). In RCP4.5, streamflow is projected to increase by 34.74% for 2029 and 18.74% for 2039, while in RCP8.5, a 37.06% decrease is expected for 2029 and 55.84% for 2039. A historical analysis indicated that there were frequent short-term droughts according to SDI-3 (3-month-period index), particularly from 2014 to 2015 and 2020 to 2021, and severe droughts according to SDI-6 (6-month-period index) in 2015 and 2020. The RCP8.5 projections indicate worsening drought conditions, with critical periods from April to June. A wavelet analysis showed that there is a significant risk of severe hydrological drought in the LCKW. Drought characteristic analysis indicated that high-intensity events occur with low frequency in the 50-year RP. Conversely, high-frequency droughts with lower intensity are observed in RPs of less than 50 years. The results of this study highlight an increase in severe drought risk in high emission scenarios, emphasizing the need for water management.

Tropical dry forests of mainland Southeast Asia contain considerable above-ground carbon (AGC) but present challenges for precise satellite-based AGC quantification because seasonal leaf phenology alters canopy reflectance throughout the year. To address this, we propose a phenology-informed approach that fuses multitemporal satellite imagery with airborne LiDAR. Using 17 PlanetScope images acquired between February 2024 and April 2026 over the Sakaerat Biosphere Reserve, together with UAV-LiDAR data, we extracted 128 phenological features and 12 canopy metrics at 10, 20 and 30 m. Machine learning models (Random Forest, XGBoost and LightGBM) were trained separately for dry evergreen forest (DEF) and dry dipterocarp forest (DDF). Under random five-fold cross-validation at 30 m, the best Random Forest models yielded R² = 0.681 (95% CI: 0.626–0.729) for DEF and R² = 0.661 (95% CI: 0.615–0.705) for DDF, with RMSE of 11.85 and 7.40 Mg C ha⁻¹, respectively. Because the AGC reference labels are themselves back-calculated from LiDAR canopy height, these Combined values partly reflect allometric circularity between predictors and labels and should be read as an upper bound rather than an independent accuracy; the spectral-only PlanetScope models, which are free of this circularity, give a more conservative R² = 0.342 (DEF) and 0.473 (DDF). Multitemporal phenological features and per-forest stratification jointly outperformed single-date baselines by 3.4× in DEF and 2.0× in DDF. We produced a 30 m AGC map of the reserve (total = 0.217 Tg C) and a higher resolution 3 m layer comprising ~8.7 million pixels. The results demonstrate the value of phenology-informed features and forest-type stratification for accurate AGC mapping in seasonally dry tropical forests, marking a step forward for remote sensing carbon assessment in phenologically dynamic landscapes.

Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m²) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R² = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R² = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher’s exact p < 1 × 10⁻¹⁵), enabling a fully automated anchor-detection pipeline.

Exploiting the complementary views of ground-based and aerial laser scanning requires co-registering their point clouds, which is difficult in closed tropical forest where Global Navigation Satellite System (GNSS) reception fails. We co-registered handheld Simultaneous Localization and Mapping (SLAM) LiDAR and UAV LiDAR without per-tree GNSS, using girth-identity anchors inherited from a companion inventory, over an open dry dipterocarp forest (DDF) and a closed dry evergreen forest (DEF) at the SERS, northeastern Thailand. Coarse-to-fine registration reduced the residual to a mean absolute distance of 0.17–0.18 m, and the clouds sampled complementary strata-stems and understory to about 25–30 m, emergent canopy to about 37 m. The co-registered data give each tree a ground-measured stem diameter and an aerially measured height, which we used to test whether diameter can instead be inferred from the canopy. It cannot: both a height-based and a best-case crown-and-height model saturated near 50 cm while measured stems reached about 100 cm, so UAV-only individual-tree carbon did not track the field reference; supplying the ground-measured diameter restored agreement (R² = 0.91), validated against reflective-tape anchor trees. The horizontal offset between a stem base and its crown apex further explains why position-only stem-to-crown matching fails and why girth-identity linkage is needed. We conclude that credible individual-tree carbon in closed tropical forest requires ground measurement of stem diameter, made possible by GNSS-independent SLAM-UAV co-registration; the aerial platform contributes height and coverage, not diameter.

Flood inundation modelling in low-gradient monsoon floodplains requires a physically consistent representation of rainfall–runoff–inundation processes. This study develops a hybrid modelling framework that integrates a coupled hydrological–hydraulic model (HEC-HMS–HEC-RAS) with a deep learning–based LSTM–U-Net surrogate to represent temporal hydrological memory and spatial inundation patterns. The framework is applied to the Upper Songkhram River Basin in northeastern Thailand, a storage-dominated floodplain strongly influenced by monsoon hydrology. The hydrological model demonstrated strong validation performance (NSE = 0.896, KGE = 0.827, R² = 0.909), while hydraulic simulations showed high spatial agreement with satellite-derived inundation maps (F1 = 0.876, Kappa = 0.873). Trained on hydraulically simulated discharge–inundation pairs, the LSTM–U-Net model successfully reproduced two-dimensional flood patterns across independent flood events (mean F1 = 0.838, IoU = 0.721), with prediction errors mainly occurring along shallow floodplain margins. Future projections under CMIP6 SSP2-4.5 and SSP5-8.5 indicate substantial increases in flood-season discharge (up to ~80%), whereas maximum inundation extent expands more moderately (≤21%), reflecting nonlinear floodplain response in low-gradient systems. The proposed framework preserves hydrological–hydraulic consistency while supporting future flood inundation projection, climate-informed flood risk assessment, and adaptation planning.