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Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential using present-day discharge from the CaMa-Flood model (2011-2015, 0.05°) and future discharge from the WaterGAP2 model used for ISIMIP2b (2021-2040, 0.5°). The dam count of 1055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1242 MW in the 1980s to 69 199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+16 854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+18 223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1 334 683 MW, where Laos is the highest at 514 887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2 000 000 MW. Laos and China are the highest at around 900 000 MW each, together forming over 80 % of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better plannin

The escalating frequency of climate change-induced droughts poses a severe threat to rainfed maize cultivation in Thailand’s upper Nan River Basin (NRB). Utilizing the standardized precipitation evapotranspiration index, this study comprehensively examines spatial and temporal drought patterns and their potential agricultural impact. Findings indicate a significant shift in precipitation patterns with wetter wet seasons, drier dry seasons and rising temperatures. The upper NRB experiences prolonged and severe droughts, while the lower region faces higher drought intensity, signalling an increased likelihood of extended and severe drought episodes in the upper region. Assessing maize cultivation suitability, factoring in environmental variables and drought impact under observed and climate change scenarios, reveals the current moderate suitability at 42.2%, projected to expand, and unsuitable regions expected to double. Different shared socioeconomic pathways (SSPs) show varied outcomes, with SSP5-8.5 indicating increased suitability in highly suitable areas and SSP2-4.5 demonstrating improvements in moderately suitable areas. The study underscores the need for tailored adaptation strategies in water management during droughts to enhance crop production, especially in dry seasons, in the upper NRB amid a changing climate.

Teleconnection events can influence normal regional weather patterns and affect weather forecast accuracy. To improve the forecast ability, the relationship between main teleconnections such as El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden–Julian Oscillation (MJO), and climate variables (rainfall, maximum and minimum surface temperature, vertical mixing ratio, and vertical maximum temperature) was established using lag correlation coefficient and t-test methods. The results reveal moderately significant correlations between El Niño, positive IOD and rainfall, and vertical mixing ratio, which can be associated with lower-than-usual rainfall. The coincidence between El Niño and positive IOD events can worsen drought. Even though the MJO and regional weather correlations were significant, the magnitude of correlation coefficients was negligible. In addition, the spatiotemporal distribution of ENSO shows that the strong El Niño has more influence on rainfall anomalies in the post-1980s. Since there are insufficient studies on the association between teleconnections and climate variables, especially vertical mixing ratio, our findings can benefit prediction development for teleconnection-induced regional climate anomalies for extreme events and water management preparations in northern and northeastern Thailand.

Projecting floods and droughts characteristics under climate change is important to formulate an integrative management plan and enhance resiliency of society. However, studies that provide the integration of floods-drought hazards are scarce within literature. This study assessed flood and drought hazards separately and together for future climate in the Mun River basin, a tributary of the Mekong river. A non-modelling and multi-variate approach was used to assess flood and drought hazard respectively. Climate model ensemble suggests that the area under ‘high’ and ‘very high’ drought hazard level will increase from 27% and 4% during baseline period (1981–2010) to 43% and 37%, respectively, during near-future period (2021–2050). Similarly, an increase in ‘high’ and ‘very high’ flood hazard from 11% and 22% during baseline period to 16% and 24% during near-future period is projected. When both hazards are considered together, the total hazard is projected to increase by 155% in the near-future period. 76% of the catchment during the near future period will have combined hazard level from ‘medium’ to ‘very high’ compared to the 30% during the baseline period. The research presents a grim outlook on for the basin, with the area at risk from both hydro-meteorological hazards.

Urbanization continues to increase in countries with tropical climates and this trend, combined with the likely increasing frequency of extreme rainfall events due to a changing climate, places such development at risk and in need of resiliency assessment. Conceptual models to assess runoff dynamics can be an important component of resiliency assessment, but there are comparatively less data to calibrate these models than are available in the global north. As such, there also is less information with respect to the drivers of model uncertainty and sensitivity. To address this gap in knowledge, we summarize the calibration results of PCSWMM for subcatchment areas in a tropical climate study catchment for which there are substantial rainfall and runoff data. Subsequently, we used the calibrated model to evaluate the impact that rain gauge density may have on runoff estimates. We also investigated the sensitivity of PCSWMM peak flow and total volume estimates to physical subcatchment parameters other than rainfall. With between 38 and 87 events captured for each monitoring station, the NSE, r2, and ISE ratings varied, but generally were in the respective ranges 0.7-0.8, 0.79-0.85, and good-excellent. It can be concluded that PCSWMM performed well in representing the tropical storm events. The rainfall pattern in the study catchment exhibited considerable spatial variability, both annually and seasonally, with annual rainfall increasing from 2063 mm near the coast to 3100 mm less than 17 km further inland. While the model was sensitive to %imperviousness, subcatchment width, impervious Manning’s n, and, to a lesser extent, various surface storage and infiltration parameters, the spatial variability of rainfall had the greatest impact on model uncertainty.