Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.

The assessments of flood‐prone areas and flood risk due to pluvial flooding for Davao Oriental on Mindanao Island in the Philippines were carried out by the analytic hierarchy process (AHP) and maximum entropy (Maxent) models using multiple criteria such as slope, elevation, soil type, rainfall, drainage density, distance to the main channel, and population density. Flood records from 70 survey points were obtained and used to verify the model results. The criteria weights of the top three important factors in the AHP are rainfall (42%), slope (23%), and elevation (15%), whereas those in the Maxent model are elevation (36%), rainfall (23%), and soil (19%). The verification results show that the accuracies of the AHP and Maxent model are 81 and 95.6%, respectively, indicating that both approaches are reliable in flood hazard and risk assessments. Approximately 22% of the total area and approximately 30% of the total population of Davao Oriental are classified as high risk of pluvial flooding in the current situation by the AHP method. This study shows a broad‐scale high‐level data‐driven screening method that can be used to help identify potential hot spots for pluvial flooding for which more detailed numerical modelling studies should be undertaken.

The riverine ecosystems of the Mekong River Basin possess the world’s most productive inland fishery and provide highly productive food crops for millions of people annually. The development of hydropower potential in the Mekong River has long been of interest to governments in the region. Among the existing 64 dams, 46 dams have been built in the Lower Mekong Basin (LMB) to produce up to 8650 MW of electricity. Additionally, of the 123 proposed built hydropower dams, eleven hydropower plants have been nominated for the river mainstream and are expected to install a total of 13,000 MW in the LMB countries. However, serious concerns have intensified over the potential negative economic consequences, especially on fisheries and agriculture in Cambodia and Vietnam. To date, most of the concerns have concentrated on the impacts on hydrology, environment, livelihood, and diversity in the LMB attributed to hydropower development. This paper, however, discusses the fishery and agricultural sectors of the LMB and focuses on the downstream floodplains of Cambodia and Vietnam. The dam construction has caused greater losses of biodiversity and fisheries than climate change in the LMB. The reduction of 276,847 and 178,169 t of fish, 3.7% and 2.3% of rice, 21.0% and 10.0% of maize will contribute to a decrease of 3.7% and 0.3% of the GDP of Cambodia and Vietnam, respectively. Lao PDR may benefit the most revenue from electricity generation than the other country in the LMB, as most of the proposed dams are projected in the country. Cambodia burdens 3/4 of the reduction of total capture fishery destruction, whilst Lao PDR, Thailand, and Vietnam endure the remaining 1/3 losses. The tradeoff analyses reveal that losses of capture fisheries, sediment or nutrients, and social mitigation costs are greater than the benefits from electricity generation, improved irrigation, and flood control of the LMB region. The socioeconomic and environmental damage caused by hydropower dams in developing countries, including the Mekong, is greater than the early costs in North America and Europe. It is proposed that dam construction for hydropower in the Mekong River, as well as other rivers in developing countries, should be gradually removed and shifted toward solar, wind, and other renewable resources.

The relationships between observed rainfall, El Niño/Southern Oscillation (ENSO) and sea surface temperature (SST) variations in the Pacific and Indian Oceans were analyzed using a 1° latitude–longitude grid over Indonesia. The Global Summary of the Day rainfall records provide 26 years of rainfall data (January 1985 to August 2010) for 23 stations throughout the Indonesian islands. The ENSO and SST variations were calculated using the Multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), NINO1 + 2, NINO3, NINO3.4, NINO4, the Dipole Mode Index (DMI) for the Indian Ocean Dipole (IOD), and Indian Ocean Basin-wide (IOBW) index. The results show that the rainfall in the southern Sumatra and southern Java Islands, which face the Indian Ocean, was positively correlated with the negative IOD, whereas the rainfall in northwestern Sumatra was positively correlated with the positive IOD. In eastern Indonesia, the rainfall was positively correlated with La Niña. The PDO index was also strongly correlated with the rainfall in this region. In central Indonesia, seasonal variations due to monsoons are predominant, and the rainfall exhibited strong negative and positive correlations with the MEI and NINO.WEST, respectively, indicating that high rainfall occurred during strong La Niña episodes. The highly negative and positive correlations with the MEI and NINO.WEST, respectively, in central Indonesia led us to analyze the impacts of Indonesian seas on the rainfall in the region. Using four synoptic-scale scenarios, we investigated the relative residence time of Indonesian seawater along the pathways associated with the Pacific-Indian hydraulic head difference. The results show that when both the western Pacific and eastern Indian Oceans are warm (positive NINO.WEST and negative DMI), the rainfall intensity over central Indonesia is strongest. This increase is explained by the relationship between the residence time of Indonesian seawater and the exposure to surface evaporation: a longer exposure of Indonesian seas results in greater seawater warming and evaporation and more convection-generated cumulus clouds.

In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km²) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.