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.

Based on a study of research and experiences in Thailand, this paper provides an overview of the implementation and challenges of watershed management, as well as potential solutions. The review found hierarchical ambiguity, inconsistency, and asynchrony across rules, as well as a lack of (participation, synchronization, and coordination) among watershed management stakeholders. Deficitsin the planning phase include a lack of integration between sectors, a lack of community participation, and a limited willingness to integrate watershed planning into regional planning. Stakeholder participation is also essential for the successful implementation of degraded watershed rehabilitation, including in terrestrial forest and mangrove regions. Failure should be minimized by providing adequateknowledge about degraded watershed characteristics, selecting appropriate plant species, and employing good mechanical construction techniques for soil and water conservation. Increase public understanding of the importance of a sustainable watershed and provide opportunities for community involvement in each phase of watershed management to achieve community participation as the majordriver of watershed management. Another issue is data gaps, which must be addressed from the designthrough the evaluation stages. The gaps can be filled using remotely sensed data and hydrologically based modeling models. Simplified watershed assessment criteria may also be required, depending on site-specific challenges and the extent of the watershed.

Increasing food demand and climate change-induced natural disasters pose significant challenges to food security. This study examines how forest conservation can mitigate disaster risks to agricultural land in Northern Thailand’s watershed. The Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center’s-River Analysis System (HEC-RAS) models were used to assess flood impacts, while the standardized precipitation and evapotranspiration index (SPEI), vegetation health index (VHI), and standardized streamflow index (SSI) evaluated drought impacts, incorporating two Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections and five land-use scenarios. Historical data shows drought causing more yield loss than floods for rice and maize, a trend expected to continue. Under future Representative Concentration Pathways (RCP) 8.5, potential rice yield loss due to drought could reach 1,834 tons and maize yield loss 7,702 tons. Flood-induced losses are lower, with potential rice yield loss at 26.2 tons and maize at 16.9 tons. Reforestation can reduce these losses by up to 25% for drought and 20% for floods. Maintaining forests in mountainous and upstream watershed areas is essential to ensure food security. Policymakers should prioritize conserving these critical areas for effective water regulation and disaster risk reduction. Forests in these areas play a crucial role in regulating water flow, reducing runoff, and enhancing soil moisture retention, which is vital for mitigating the impacts of extreme weather events on agriculture. Keywords: climate change, drought impact, ecosystem-based disaster risk reduction, flood impact, food security

This study analyzes future hydrological droughts in LCKW using the SWAT model and data from 2010-2021, evaluating the Streamflow Drought Index (SDI) for 5, 10, 25, and 50-year return periods (RP) for 2029 and 2039 under RCP4.5 and RCP8.5 scenarios. The model shows high accuracy (R² = 0.82, NSE = 0.78). Under RCP4.5, streamflow may increase by 34.74% and 18.74%, while RCP8.5 predicts significant decreases of 37.06% and 55.84%. Historical data reveal frequent short-term droughts during SDI-3, notably in 2014–2016 and 2020–2021, with SDI-6 indicating less frequent but severe droughts, especially in 2015 and 2020. Future projections under RCP8.5 suggest wors-ening drought conditions in 2029 and 2039, with critical drought periods from January to April and November. Wavelet analysis indicates a high risk of severe hydrological drought in LCKW due to low annual recurrence rates, with the highest drought severity over a 50-year RP. The study high-lights an increasing likelihood of frequent and severe droughts, particularly under high emission scenarios such as RCP8.5, which could result in significant water shortages and ecosystem disrup-tions. It emphasizes the significance of adaptive water resource management strategies in mitigating negative impacts on water security and ecosystem health.

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.