The rate of deforestation declined steadily in Thailand since the year 2000 due to economic transformation away from forestry. However, these changes did not occur in Nan Province located in northern Thailand. Deforestation is expected to continue due to high demand for forest products and increased agribusiness. The objectives of this paper are (1) to predict land-use change in the province based on trends, market-based and conservation scenarios, (2) to quantify biodiversity, and (3) to identify biodiversity hotspots at greatest risk for future deforestation. This study used a dynamic land-use change model (Dyna-CLUE) to allocate aggregated land demand for three scenarios and employed FRAGSTATS to determine the spatial pattern of land-use change. In addition, the InVEST Global Biodiversity Assessment Model framework was used to estimate biodiversity expressed as the remaining mean species abundance (MSA) relative to their abundance in the pristine reference condition. Risk of deforestation and the MSA values were combined to determine biodiversity hotspots across the landscape at greatest risk. The results revealed that most of the forest cover in 2030 would remain in the west and east of the province, which are rugged and not easily accessible, as well as in protected areas. MSA values are predicted to decrease from 0.41 in 2009 to 0.29, 0.35, and 0.40, respectively, under the trends, market-based and conservation scenarios in 2030. In addition, the low, medium, and high biodiversity zones cover 46, 49 and 6% of Nan Province. Protected areas substantially contribute to maintaining forest cover and greater biodiversity. Important measures to protect remaining cover and maintain biodiversity include patrolling at-risk deforestation areas, reduction of road expansion in pristine forest areas, and promotion of incentive schemes for farmers to rehabilitate degraded ecosystems.Sustainability 2019, 11, 649 2 of 22 or NESDP [4] focused on commercial crops for international trade. This national policy resulted in a rapid increase of cultivated land area by two-fold, from 15% in 1961 to 31% in 1980 [5]. In addition, the agricultural sector contributed more than 25% of the Gross Domestic Product (GDP) of Thailand [6] and more than 70% of the total population worked in the agricultural sector [7].Since 1980, Thailand's national policies have shifted from an agriculture-based economy to manufacturing and service sectors. Thailand is currently in the fourth stage (Thailand 4.0) in which the economy moved towards value-based and innovative products [8]. The contribution of the service sector now accounts for over 50% of the total GDP. In contrast, the contribution of the agricultural sector dropped below 10% after 1990 [6]. Nevertheless, a large proportion of the labor force (56% of total population) is involved in the agricultural sector [7].Agricultural and economic development policies resulted in both positive and negative consequences. In addition, agricultural policies largely maximize economic return, while degrade b...

Thailand plays a central economic and policy-making role in Southeast Asia. Although climate change adaptation is being mainstreamed in Thailand, a well-organized overview of the impacts of climate change and potential adaptation measures has been unavailable to date. Here we present a comprehensive review of climate-change impact studies that focused on the Thai water sector, based on a literature review of six sub-sectors: riverine hydrology, sediment erosion, coastal erosion, forest hydrology, agricultural hydrology, and urban hydrology. Our review examined the long-term availability of observational data, historical changes, projected changes in key variables, and the availability of economic assessments and their implications for adaptation actions. Although some basic hydrometeorological variables have been well monitored, specific historical changes due to climate change have seldom been detected. Furthermore, although numerous future projections have been proposed, the likely changes due to climate change remain unclear due to a general lack of systematic multi-model and multi-scenario assessments and limited spatiotemporal coverage of the study area. Several gaps in the research were identified, and ten research recommendations are presented. While the information contained herein contributes to state-of-the-art knowledge on the impact of climate change on the water sector in Thailand, it will also benefit other countries on the Indochina Peninsula with a similar climate.

Most Asian countries are undergoing reforms to bring improvements in local environmental governance. Decentralization, private-sector participation, and community participation are the major reforms contributing toward good governance, including in urban environmental management. Increasing demand for adequate environmental services and effective natural resource protection, because of rapid urbanization and economic growth, is pushing national and local agencies to accelerate reform. This article reviews environmental reforms and their practical applications in solid waste management, water supply and wastewater management, and air quality management in 14 Asian cities. There have been considerable improvements in the quality and coverage of urban environmental infrastructure and services in these cities. These improvements can be further optimized by improving the combination of reforms for the targeted service: building the capacity of local government institutions, community enterprises, and civil society; and modifying initiatives and programs to address diversified needs and characteristics within the same city.

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Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans.

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Recently, severe floods and drought, caused by the global climate change, have occurred in various places around the world. In Asia, in monsoon regions having clear rainy and dry seasons, the water cycle will be accelerated as global warming proceeds, resulting in more intense rainfall and long-term drought. In Indonesia, an increase of food production is needed to accommo

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