Interdecadal Variability in Myanmar Rainfall in the Monsoon Season (May–October) Using Eigen Methods

The Role of Climate Change and Its Sensitivity on Long-Term Standardized Precipitation Evapotranspiration Index, Vegetation and Drought Changing Trends over East Asia

Droughts have become more severe and frequent due to global warming. In this context, it is widely accepted that for drought assessments, both water supply (rainfall) and demand (standardized precipitation evapotranspiration index, SPEI) should be considered. Using SPEI, we explored the spatial-temporal patterns of dry and wet annual and seasonal changes in five sub-regions of East Asia during 1902-2018. These factors are linked to excess drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change studies. Our results show that the SPEI significantly improved extreme drought and mostly affected the SPEI-06 and SPEI-12 growing seasons in East Asia during 1981-2018. The dry and wet annual SPEI trends mostly affect the five sub-regions of East Asia. The annual SPEI had two extremely dry spells during 1936-1947 and 1978-2018. Japan, South Korea, and North Korea are wet in the summer compared to other regions of East Asia, with drought frequency occurring at 51.4%, respectively. The mean drought frequencies in China and Mongolia are 57.4% and 54.6%. China and Mongolia are the driest regions in East Asia due to high drought frequency and duration. The spatial seasonal analysis of solar radiation (SR), water vapor pressure (WVP), wind speed (WS), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) have confirmed that the East Asia region suffered from maximum drought events. The seasonal variation of SPEI shows no clear drying trends during summer and autumn seasons. During the winter and spring seasons, there was a dry trend in East Asia region. During 1902-1990, a seasonal SPEI presented diverse characteristics, with clear wet trends in Japan, Mongolia, and North Korea in four different growing seasons, with dry trends in China and South Korea. During 1991-2018, seasonal SPEI presented clear dry trends in Japan, Mongolia, and North Korea in different growing seasons, while China and South Korea showed a wet trend during the spring, autumn, and winter seasons. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within East Asia. An understandings of long-term vegetation trends and the effects of rainfall and SPEI on droughts of varying severity is essential for water resource management and climate change adaptation. Based on the results, water resources will increase under global warming, which may alleviate the water scarcity issue in the East Asia region.

Patterns of floristic inventory and plant collections in Myanmar

Myanmar is one of the most biodiverse countries in the Asia-Pacific region due to a wide range of climatic and environmental heterogeneity. Floristic diversity in Myanmar is largely unknown, resulting in a lack of comprehensive conservation plans. We developed a database of higher plants in Myanmar derived from herbarium specimens and literature sources, and analyzed patterns of diversity inventories and collection inconsistencies, aiming to provide a baseline floristic data of Myanmar and act as a guide for future research efforts. We collected 1,329,354 records of 16,218 taxa. Results show that the collection densities at the township level was variable, with 5% of townships having no floristic collections. No ecoregion had an average collection density of greater than 1 specimen/km2 and the lowest collection density was found in the Kayah-Karen Montane Rainforests, which covered 8% of Myanmar's total area. The highest sampling densities were found in Mandalay Region, Chin State, and Yangon Region. Despite floristic collections over the past three centuries, knowledge of the distribution of the vast majority of plant taxa remained limited, particularly for gymnosperms, pteridophytes, and bryophytes. More botanical surveys and further analyses are needed to better describe Myanmar's floristic diversity. An important strategy to promote knowledge of the biodiversity patterns in Myanmar is to improve the collection and digitalization of specimens and to strengthen cooperation among countries.

Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure and vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.

Recent Changes in Drought Events over South Asia and Their Possible Linkages with Climatic and Dynamic Factors

South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective.

Remote Sensing for Development of Rainfall Intensity–Duration–Frequency Curves at Ungauged Locations of Yangon, Myanmar

This study aims to develop the intensity–duration–frequency (IDF) curves for Yangon, the economic center of Myanmar, using four satellite precipitation datasets, namely GPM IMERG, TRMM, GSMaP_NRT, and GSMaP_GC. Different probability distribution functions were used to fit the annual rainfall maximum series to determine the best-fit distribution. The estimated parameters of the best-fit distribution were used to fit the rainfall intensities of 2, 5, 10, 25, 50, and 100-year return periods for generating IDF curves using the Sherman equation. The IDF curves were bias-corrected based on the daily rainfall data available only at a location in Yangon. The bias correction factors were then used to estimate IDF curves from satellite rainfall at ungauged locations of Yangon. The results showed that the Generalized Extreme Value Distribution best fit the hourly rainfall distribution of satellite data. Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is the most suitable for constructing Yangon’s IDF curves. The bias-corrected IDF curve generated at four locations of greater Yangon indicates higher rainfall intensity at the coastal stations than the inland stations. The methodology presented in this study can be used to derive IDF curves for any location in Myanmar.

Review of Effects of Dam Construction on the Ecosystems of River Estuary and Nearby Marine Areas

Dams have made great contributions to human society, facilitating flood control, power generation, shipping, agriculture, and industry. However, the construction of dams greatly impacts downstream ecological environments and nearby marine areas. The present manuscript presents a comprehensive review of the influence of human activities on the environment, especially the effect of dam construction on the ecosystems of river estuaries and nearby marine areas, so as to provide a scientific basis for ecological environment protection. To summarize these impacts, this review used recent studies to comprehensively analyze how dam construction has affected river hydrology, geomorphology, and downstream ecosystems globally. Effects of dams on ecosystems occur through reduced river flow, reduced sediment flux, altered water temperature, changed estuary delta, altered composition and distribution of nutrients, altered structure and distribution of phytoplankton populations, habitat fragmentation, and blocked migration routes in river sections and adjacent seas. Effects of dam construction (especially the Three Gorges Dam) on the Yangtze River were also reviewed. Performing community and mitigation planning before dam construction, exploring new reservoir management strategies (including targeted control of dam storage and flushing sediment operations), banning fishing activities, and removing unnecessary dams (obsolete or small dams) are becoming crucial tools for ecosystem restoration.

Bandung Rainfall Forecast and Its Relationship with Niño 3.4 Using Nonlinear Autoregressive Exogenous Neural Network

The city of Bandung, as the capital city of West Java, is one of several areas in Indonesia with high rainfall. This situation can cause disasters, such as floods and landslides, that can harm many parties. Rainfall in Indonesia, particularly on the island of Java itself, is closely related to the global phenomenon of Niño 3.4. In the period from January 2001–November 2021, the rainfall and Niño 3.4 showed some extreme values. In order to foresee the disasters, an accurate rainfall forecast should be performed. For this reason, we try to construct a model of rainfall forecast and its relation to the global phenomenon of Niño 3.4 using the nonlinear autoregressive exogenous neural network (NARX NN). The result shows that NARX NN (13-7-1) with a Mean Absolute Percentage Error (MAPE) value of 6.26% and R2 of 85.37% is best suited for the prediction of this phenomenon. In addition, this study provides forecast results for the next six periods, which can be used as a reference for the relevant authorities to foresee the possibility of flooding in Bandung city. From the forecast results, it can be concluded that the highest rainfall forecasts in the city of Bandung are in February 2022, and will slowly decrease in March 2022. To prevent hydro-meteorological disasters, such as floods in Bandung city, the community can clear waterways, such as clogged drains, rivers, and dams, as well as prepare tools for evacuation.