Multi-purposeful Application of Geospatial Data

Agriculture is the backbone of the Indian economy. Any changes in weather and climate in short term as well as long- term adversely affect the agricultural productivity and the production of food grain production. In order to minimise the adverse impact of weather and climate on crops, the use of agrometeorological information and agromet services has already been proved to be highly beneficial. Agrometeorological services rendered by India Meteorological Department (IMD), Ministry of Earth Sciences, are a step to contribute to weather information-based crop/livestock management strategies and operations dedicated to enhance crop production and food security. IMD is operating a project ‘Gramin Krishi Mausam Sewa’ (GKMS) with an objective to serve the farming community at different parts of the country. Different states of technologies including the application of geospatial technology are being used in India for further refinement of the Agromet Advisory Services. The application of geospatial technology in generating agrometeorological information and products is very necessary for preparing need-based advisories at a high-resolution scale for the farmers in the country. In this chapter, elaborate discussion has been made on how the Geographical Information System (GIS) is being used for generating information and products using ground observations as well as satellite observations

Role of Machine Learning, Deep Learning and WSN in Disaster Management: A Review and Proposed Architecture

Disasters are occurrences that have the potential to adversely affect a community via casualties, ecological damage, or monetary losses. Due to its distinctive geoclimatic characteristics, India has always been susceptible to natural calamities. Disaster Management is the management of disaster prevention, readiness, response, and recovery tasks in a systematic manner. This paper reviews various types of disasters and their management approaches implemented by researchers using Wireless Sensor Networks (WSNs) and machine learning techniques. It also compares and contrasts various prediction algorithms and uses the optimal algorithm on multiple flood prediction datasets. After understanding the drawbacks of existing datasets, authors have developed a new dataset for Mumbai, Maharashtra consisting of various attributes for flood prediction. The performance of the optimal algorithm on the dataset is seen by the training, validation and testing accuracy of 100%, 98.57% and 77.59% respectively

Towards a New Agriculture for the Climate Change Era in West Asia, Iran

28 páginas. El libro tiene 486 páginas.Climate change means a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods. It will potentially lead to such eventualities as drought and famine, which some of the CWANA countries have already experienced. The capacity of national governments and communities to mitigate disasters will be limited in the short to medium term, rendering them still vulnerable to the adversities of climate change. Climate change is a global issue with regional implications. Many multilateral environmental agreements address these issues, and some countries of the region have ratified some such agreements (CWANA, 2009). Effects of climate change on land use refers to both how land use might be altered by climate change and what land management strategies would mitigate the negative effects of climate change (Dale, 1997). Asia is the most populous continent, population in 2002 was reported to be about 3,902 million, of which almost 61% is rural and 38.5% lives within 100 km of the coast (Duedall & Maul, 2005). Asia is divided into seven subregions, namely North Asia, Central Asia, West Asia, Tibetan Plateau, East Asia, South Asia and South-East Asia.Peer reviewe

Mapping of risk web-platforms and risk data: collection of good practices

A successful DRR results from the combination of top-down, strategies, with bottom-up, methodological approaches. The top–down approach refers more to administrative directives, organizations, and operational skills linked with the management of the risk and reflects more the policy component. The bottom-up approach is linked to the analyse of the causal factors of disasters, including exposure to hazards, vulnerability, coping capacity, and reflects more the practice component. In the context of disaster science, policy and practice are often disconnected. This is evident in the dominant top-down DRM strategies utilizing global actions on one hand and the context specific nature of the bottom-up approach based on local action and knowledge. A way to bridge the gap between practice and policy is to develop a spatial data infrastructure of the type of GIS web-platforms based on risk mapping. It is a way of linking data information and decision support system (DSS) on a common ground that becomes a “battlefield of knowledge and actions”. This report presents the results of an overview of the risk web-platforms and related risk data used in risk assessment at the level of EU-28. It allows the discovery of the current advancement for risk web infrastructures and capabilities in order to establish a pool of good practices and detection of needs. The outcome of the overview shows the needs in risk web platform developments and tries to recommend capacities that should be prioritized in order to strengthen the link between risk data information and decision support system (DSS). The assessment is based on web search and outcome of diverse disaster risk workshops and conference.JRC.E.1-Disaster Risk Managemen

TWINLATIN: Twinning European and Latin-American river basins for research enabling sustainable water resources management. Combined Report D3.1 Hydrological modelling report and D3.2 Evaluation report

Water use has almost tripled over the past 50 years and in some regions the water demand already exceeds supply (Vorosmarty et al., 2000). The world is facing a “global water crisis”; in many countries, current levels of water use are unsustainable, with systems vulnerable to collapse from even small changes in water availability. The need for a scientifically-based assessment of the potential impacts on water resources of future changes, as a basis for society to adapt to such changes, is strong for most parts of the world. Although the focus of such assessments has tended to be climate change, socio-economic changes can have as significant an impact on water availability across the four main use sectors i.e. domestic, agricultural, industrial (including energy) and environmental. Withdrawal and consumption of water is expected to continue to grow substantially over the next 20-50 years (Cosgrove & Rijsberman, 2002), and consequent changes in availability may drastically affect society and economies. One of the most needed improvements in Latin American river basin management is a higher level of detail in hydrological modelling and erosion risk assessment, as a basis for identification and analysis of mitigation actions, as well as for analysis of global change scenarios. Flow measurements are too costly to be realised at more than a few locations, which means that modelled data are required for the rest of the basin. Hence, TWINLATIN Work Package 3 “Hydrological modelling and extremes” was formulated to provide methods and tools to be used by other WPs, in particular WP6 on “Pollution pressure and impact analysis” and WP8 on “Change effects and vulnerability assessment”. With an emphasis on high and low flows and their impacts, WP3 was originally called “Hydrological modelling, flooding, erosion, water scarcity and water abstraction”. However, at the TWINLATIN kick-off meeting it was agreed that some of these issues resided more appropriately in WP6 and WP8, and so WP3 was renamed to focus on hydrological modelling and hydrological extremes. The specific objectives of WP3 as set out in the Description of Work are

Using spatiotemporal correlative niche models for evaluating the effects of climate change on mountain pine beetle

Includes bibliographical references.2015 Summer.Over the last decade western North America has experienced the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in recorded history and Rocky Mountain forests have been severely impacted. Although bark beetles are indigenous to North American forests, climate change has facilitated the beetle’s expansion into previously unsuitable habitats. I used three correlative niche models (MaxEnt, Boosted Regression Trees, and Generalized Linear Models) to estimate: (i) the current potential distribution of the beetle in the U.S. Rocky Mountain region, (ii) how this extent has changed since historical outbreaks in the 1960s and 1970s, and (iii) how the potential distribution may be expected to change under future climate scenarios. Additionally, I evaluated the temporal transferability of the niche models by forecasting historical models and testing the model predictions using temporally independent outbreak data from the current outbreak. My results indicated that there has been a significant expansion of climatically suitable habitat over the past 50 years and that much of this expansion corresponds with an upward shift in elevation across the study area. Furthermore, my models indicate that drought was a more prominent driver of current outbreak than temperature, which suggests a change in the climatic signature between historical and current outbreaks. The current climatic niche of the mountain pine beetle includes increased precipitation, colder winter temperatures, and a later spring than the historical climatic niche, which reflects a shift into higher elevation habitats. Projections under future conditions suggest that there will be a large reduction in climatically suitable habitat for the beetle and that high-elevation forests will continue to become more susceptible to outbreak. While all three models generated reasonable predictions (AUC = 0.85 - 0.87), the generalized linear model correctly predicted a higher percentage of current outbreak localities when trained on historical data. My findings suggest that projects aiming to reduce omission error in estimates of future species responses may have greater predictive success with simpler, generalized models