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The first aim of this study is to show the impact of global warming on temperature and precipitation changes in Algeria. The second objective is to exhibit the strategy planned by the country to mitigate these effects on water resources in the future.

This research assesses the expected changes in temperature, precipitation and SPEI index, over Algeria (16 weather stations), between two horizons (2030, 2050), by using an ensemble of 16 general circulation models under RCP2.6 and RCP8.5 scenarios.

More warming and drought will be experienced under RCP8.5 than RCP2.6 scenario. The highest warming is observed at the Southern stations. However, the lowest precipitation is projected in the western stations. The results of SPEI calculation indicate that the severity of drought spread progressively across time and space. The highest values were observed over 2050 with values varied between 0.15 and −2.08 under RCP2.6 scenario, and range from −0.73 to −2.63 under RCP8.5. These results indicate that Algeria is highly vulnerable to the impact of climate change on water resources, which stressed the need to develop a strategy against this situation.

This study is one of the first to simulate the future climate changes over 16 Algerian weather stations by using an average of 16 general circulation models data, under two RCP scenarios. This study shows the 2030 water development strategy to mitigate the effect of drought and water scarcity on different sectors.

Tackling the challenges of water scarcity requires comprehensive management according to financial, environmental and social issues. This paper aims to develop a planning approach for systematic decision-making and pay attention to uncertainties in water demand management and supply investment.

This study presents a multiobjective optimization model to manage water resources based on the balance of supply and demand. The objectives of the model include economic, social and environmental (sustainable development) factors. The model achieves an optimal urban water portfolio by using a scenario tree.

The mathematical goal programming (GP) in a multiobjective optimization model is applied and solved by the branch and bound method. The results indicate the selected supply augmentation and demand management options in each stage for 20 years according to the dry, normal and wetness year scenarios.

This model is based on a real-world case and has been implemented in the city of Karaj. It can be applied for water management of other cities concerning sustainable development as well.

This paper innovates by considering the sustainable development criteria that are defined using three objective functions, including economic, social and environmental factors. The balance of supply and demand concerning uncertainty has not been investigated in any urban water portfolios. The standardized precipitation evapotranspiration index (SPEI) is incorporated to generate different scenarios. To the best of the authors’ knowledge, this approach is used for the first time.

The purpose of this study is to assess the spatiotemporal patterns of future meteorological drought in the Yellow River Basin under different representative concentration pathway (RCP) scenarios.

Delta method is used to process the future climate data of the global climate models, then analyzed the spatiotemporal variation trend of drought in the Yellow River Basin based on standardized precipitation evaporation index (SPEI) under four RCP scenarios.

This research was funded by the National Natural Science Foundation of China (41901239), Soft Science Research Project of Henan Province (212400410077, 192400410085), the National Key Research and Development Program of China (2016YFA0602703), China Postdoctoral Science Foundation (2018M640670) and the special fund of top talents in Henan Agricultural University (30501031).

This study can provide support for future meteorological drought management and prevention in the Yellow River Basin and provide a theoretical basis for water resources management.

Examine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.

Based on 0.5° gridded rainfall and temperature data (1960–2015) and projections of the WRF-RCM (2016–2040), the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are calculated for Central Java over different time spans. The drought indices are correlated to annual and seasonal rice production, based on which a weather index insurance structure is developed.

The six-month SPI correlates best with the wet season rice production, which generates most output in Central Java. The SPI time series reveals that drought severity increases in future years (2016–2040) and leads to higher payouts from the weather index structure compared to the historical period (1960–2015).

The developed methodology in using SPI for historical and projected periods allows the development of weather index insurance in other regions which have a clear link between rainfall deficit and agricultural production volatility.

Meteorological drought indices are a viable alternative for weather index insurance, which is usually based on rainfall amounts. RCM outputs provide valuable insights into future climate variability and drought risk and prolong the time series, which should result in more robust weather index insurance products.

Using risk-related data often require a significant amount of upfront work to collect, extract and transform data. In addition, the lack of a consistent data structure hinders the development of tools that can be used with more than one set of data. The purpose of this paper is to report on an effort to solve these problems through the development of extensible, internally consistent schemas for risk-related data.

The consortia coordinated their efforts so the hazard, exposure and vulnerability schemas are compatible. Hazard data can be provided as either event footprints or stochastic catalogs. Exposure classes include buildings, infrastructure, agriculture, livestock, forestry and socio-economic data. The vulnerability component includes fragility and vulnerability functions and indicators for physical and social vulnerability. The schemas also provide the ability to define uncertainties and allow the scoring of vulnerability data for relevance and quality.

As a proof of concept, the schemas were populated with data for Tanzania and with exposure data for several other countries.

The data schema and data exploration tool are open source and, if widely accepted, could become widely used by practitioners.

A single set of hazard, exposure and vulnerability schemas will not fit all purposes. Tools will be needed to transform the data into other formats.

This paper describes extensible, internally consistent, multi-hazard, exposure and vulnerability schemas that can be used to store disaster risk-related data and a data exploration tool that promotes data discovery and use.

The purpose of this paper is to investigate the patterns and trends of drought incidence in north east highlands of Ethiopia using monthly rainfall record for the period 1984-2014.

Standard precipitation index and Mann – Kendal test were used to analyze drought incident and trends of drought occurrences, respectively. The spatial extent of droughts in the study area has been interpolated by inverse distance weighted method using the spatial analyst tool of ArcGIS.

Most of the studied stations experienced drought episodes in 1984, 1987/1988, 1992/1993, 1999, 2003/2004 and 2007/2008 which were among the worst drought years in the history of Ethiopia. The year 1984 was the most drastic and distinct-wide extreme drought episode in all studied stations. The Mann–Kendal test shows an increasing tendencies of drought at three-month (spring) timescale at all stations though significant (p < 0.05) only at Mekaneselam and decreasing tendencies at three-month (summer) and 12-month timescales at all stations. The frequency of total drought was the highest in central and north parts of the region in all study seasons.

This detail drought characterization can be used as bench mark to take comprehensive drought management measures such as early warning system, preparation and contingency planning, climate change adaptation programs.

This paper aims to document the adaptation strategies developed by local farmers to adjust to climate change and related hazards in Lijiang Prefecture in Southwest China, and quantify the determinants of the adaptation measures.

The study conducted a household survey with 433 respondents in Lijiang to documents adaptation measures. The authors used a multivariate probit model to quantify five categories of adaptation measures against a set of household features, extension and information, resources, social network, financial assets and perception variables.

The most significant determinants consisted of information on early climate warnings and impending hazards, ownership to land and livestock, irrigation membership in community-based organisations, household savings, cash crop farming and perceptions of climate change and its related hazards. Adaptation strategies and policies highlighting these determinants could help to improve climate change adaptation in the region.

This study quantified the determinants of adaptive strategies and mapped important determinants for the region that will provide farmers with the appropriate resources and information to implement the best practices for adapting to climatic changes. The method and findings could be useful and easily replicable for future agriculture policies.

Climate change is most apparent through the increased severity and frequency of extreme events. Tourism as an activity is particularly sensitive. This paper aims to investigate the impact that climate change has on Xiamen tourism through a fuzzy comprehensive evaluation of questionnaire responses.

A fuzzy classification system of tourism factors most sensitive to climate change was built on the basis of an analytical hierarchical process.

A “relatively strong” association grade of the impacts of climate change on tourism was observed. Through fuzzy comprehensive evaluation, the method used has allowed for clear classification of the aspects of tourism, through its development, which are more vulnerable to climate change. The results acquired here can serve as reference material for stakeholders on implementing risk assessments, deepening the understanding of how climate change affects tourism and coordinate the interests of different parties through the achievement of focused development and realize the optimum, long-term and sustainable exploitation of tourism resources.

The sensitivity of a variety of tourist sectors within Xiamen was assessed and represents the newest pre-COVID-19 opinions concerning the effect of climate change on tourism. Additionally, the data used in this study was also collected before the outbreak of the COVID-19 pandemic and will serve as an important marker to track how expert opinions of the effects of climate change on tourism change over time.

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.