Precise spatio-temporal measurements of rainfall during seasonal monsoons are critical for accurate hydrologic analyses in the tropical regions of Southeast Asia. The use of satellite precipitation data is technologically sound but requires downscaling to minimize inherent uncertainties. The uncertainties at a local climate regime that are essential to be resolved are rarely reported; consequently, such work needs attention. To address this problem, we validated the Tropical Rainfall Measuring Mission (TRMM) precipitation data using high-resolution areal precipitation (0.125 deg.) at a seasonal scale. This study examined the performance of the monthly rainfall data product (TRMM 3B43) at the seasonal monsoon scale in the local climate region of Peninsular Malaysia. The high-resolution areal precipitation data (0.125 deg.) were derived from a dense rain gauge network ever collected in Peninsular Malaysia (n = 984). Three relevant performance elements were evaluated: (i) the ability to depict temporal rainfall variation (ii) the quantitative error between TRMM and ground rainfall; and (iii) the ability to estimate the actual rainfall amount. We found that the ability of monthly TRMM data to depict rainfall variation and its tendency to propagate large errors varied seasonally. The correlation between TRMM and ground rainfall was good during the wettest period in all local climate regions. The error was related to the northeast monsoon and inter-monsoon 2 (September–October). Meanwhile, the TRMM ratio varied regionally, rather than seasonally. Determining the local-scale uncertainties will facilitate future downscale activities using TRMM satellite data in this region.

This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998–2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent.

Soil Water Index (SWI) represents the conceptual water stored in the soil and is calculated using a three-layer tank model with hourly precipitation. In Japan, landslide disasters are likely to occur when SWI in an event exceeds the maximum value of the past 10 years; however, snowmelt-driven landslide disasters have not been considered yet. Using the tank model that simultaneously calculates SWI and runoff, we implemented the snowfall-accumulation-snowmelt processes into the original SWI and applied the modified SWI to meteorological data in Tomsk, Russia, in spring 2010 when severe flood and landslide disasters had occurred. We conducted a sensitivity analysis of hourly precipitation in snowy region in Japan considering that meteorological data in Russia are available every 3 h. When we input the average of the three-hourly accumulated precipitation to calculate SWI, the result was almost identical to that of the observed hourly precipitation being given. We then estimated the hourly temperature by linearly interpolating the data every 3 h, and set the threshold of liquid/solid precipitation. The degree-hour method was employed to calculate the snowmelt. The modified SWI predicted the occurrence of snowmelt-driven landslide disasters in Japan when the calculated SWI exceeded the maximum value in the snowmelt season (March–May) for the past 10 years. When applied to meteorological data in Tomsk, the modified SWI and calculated runoff captured the timing of snowmelt-driven flood and landslide disasters in spring 2010. We demonstrated that by focusing on the maximum value of SWI in the snowmelt season for the past 10 years, we can predict snowmelt-driven landslide disasters.

Siberia, which experienced disastrous heat waves in 2010 and 2012, is one of the regions in which extreme climate events have occurred recently. To compare the long-term trends of extreme climate events in the southern part of Siberia with those of surrounding regions, we calculated 11 extreme climate indexes from observational data for 1950–2019 and analyzed the trends in Siberia and other parts of Russia using statistical techniques, i.e., Welch’s t-test, the Mann–Kendall test, Sen’s slope estimator, and a cluster analysis. We clarified that high-temperature events in March are more frequent in Siberia than in the surrounding areas. However, the increasing trends of high temperatures in Siberia were lower than those in northwestern China and Central Asia. The intensity of heavy precipitation is increasing in Siberia, as it is in the surrounding areas. Compared to the surrounding areas analyzed in previous studies, the trend of heavy precipitation in Siberia has not increased much. In particular, Siberia shows a more remarkable decreasing trend in heavy precipitation during the summer than other regions. The dry trends in the summer, however, do not occur in Siberia as a whole, and the opposite trend of summer precipitation was observed in some areas of Siberia.