Uncertainty in hydrological statistics estimated with finite observations, such as design rainfall, can be quantified as a confidence interval using statistical theory. Ensemble climate data also enables derivation of a confidence interval. Recently, the database for policy decision making for future climate change (d4PDF) was developed in Japan, which contains dozens of simulated extreme rainfall events for the past and 60 years into the future, allowing the uncertainty of design rainfall to be quantified as a confidence interval. This study applies an order statistics distribution to evaluate uncertainty in the order statistics of extreme rainfall from the perspective of mathematical theory, while a confidence interval is used for uncertainty evaluation in the probability distribution itself. An advantage of the introduction of an order statistics distribution is that it can be used to quantify the goodness-of-fit between observation and ensemble climate data under the condition that the extreme value distribution estimated from observations is a true distribution. The order statistics distribution is called the control density distribution, which is derived from characteristics that order statistics from standard uniform distribution follows beta distribution. The overlap ratio of the control density distribution and frequency distributions derived from ensemble climate data is utilized for evaluation of the degree of goodness-of-fit for both data.

In northeast Thailand, 17% of the total agricultural land is classified as salt-affected. In the future, climate change may exacerbate salt-affected soil problems. Therefore, in this study, we conducted a field survey to evaluate seasonal changes in soil electrical conductivity (ECe) in salt-affected paddy areas of Ban Phai District, Khon Kaen Province, northeast Thailand. Fifteen soil samples were collected every 2 weeks from October 2016 to December 2018, and the ECe, soil water content, and soil textures were analyzed. Then, the HYDRUS-1D model was applied to estimate seasonal changes in the salinity level, and the simulated results corresponded well with observed data. Using HYDRUS-1D and the global circulation model (MIROC5) outputs under the Representative Concentration Pathways 8.5 scenario, future ECe was predicted. Under a temperature increase of 2.8°C from 2016 to 2100, annual potential evapotranspiration increased from 1,430 mm (2016–2025) to 1,584 mm (2081–2100). The average ECe in cultivation season increased from 2.63 dS/m (2016–2025) to 3.31 dS/m (2081–2100). As a countermeasure to mitigate soil salt accumulation, a 5 cm reduction in groundwater level offsets the negative impact of climate change, and a 10 cm reduction significantly improves the soil ECe relative to the current soil salinity level.

The surface water formation parameter (K_w) currently used in malaria transmission models can dramatically affect larval development calculations. However, the parameter is often unrealistic due to the unavailability of observational datasets. This research presents an adjusted K_w by reference to an entomological inoculation rate (EIR) over the period 1983–2006, tuning the parameter by minimizing root mean square deviation of the water fraction from model calculations and satellite observations from 2014–2018. A scaling factor, topography factor, and inverse distance weighting were used to reduce the gap between macro- and microscales and to derive the appropriate spatial distribution of K_w for a projection period from 2020–2100. The average EIR over the projection period under Representative Concentration Pathway (RCP) scenarios 2.6, 7.0, and 8.5 in West Africa decreased by –29%, –43% and –35%, respectively, from the historical period. By contrast, for central southern Africa, the respective values increased by 3%, 6%, and 29% from the historical period. The reduced EIRs under RCPs 7.0 and 8.5 in West Africa were mostly affected by temperature, while monthly mean precipitation triggered a decrease in EIRs under RCP 2.6. By contrast, consecutive wet days have the most influential role in increasing the EIR in central southern Africa under all RCP scenarios. This research will help policy-makers eradicate vulnerable malaria areas and improve related policy design.