Towards a natural disaster intervention and recovery framework

Contemporary responses to facilitate long‐term recovery from large‐scale natural disasters juxtapose between those of humanitarian agencies and governments and those of the affected community. The extent to which these mechanisms articulate is crucial to the recovery propensity of the affected communities. This research examines such action by exploring the relationship between the scale of post‐disaster response interventions, the extent of community participation in them, and their impact on community recovery, using a community wealth capital framework. The investigation was applied to a study of the longer‐term community recovery of the island of Vilufushi, Republic of Maldives, which was almost completely destroyed by the Indian Ocean tsunami of 26 December 2004. Data were analysed through the employment of a pattern match technique and a holistic recovery network analysis. The research framework, informed by the case‐study results, other long‐term recovery evaluations, and existing resilience theory, is reconfigured as a testable roadmap for future post‐disaster interventions.     

The nuclear disaster management system in Taiwan: a case study of the third (Maanshan) nuclear power plant

This paper explores the effectiveness of the nuclear disaster management system in Taiwan via a review of the third (Maanshan) nuclear power plant. In doing so, the Fukushima Daiichi nuclear disaster in Japan on 11 March 2011 is reviewed and compared with the situation in Taiwan. The latter's nuclear disaster management system is examined with respect to three key variables: information; mobilisation; and inter‐organisational cooperation. In‐depth interviews with 10 policy stakeholders with different backgrounds serve as the research method. The results point up the need for improvement in all dimensions. In addition, they highlight three principal problems with the nuclear disaster management system: (i) it might not be possible to provide first‐hand nuclear disaster information immediately to the communities surrounding the Maanshan facility in Pingtung County, southern Taiwan; (ii) the availability of medical resources for treating radiation in Hengchun Township is limited; and (iii) the inter‐organisational relationships for addressing nuclear disasters need to be strengthened. Hence, cooperation among related organisations is necessary.     

A framework to minimise total energy consumption and total tardiness on a single machine

A great amount of energy is wasted in industry by machines that remain idle due to underutilisation. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimisation of energy consumption objective while making scheduling decisions. To minimise energy consumption, the decision maker has to decide the timing and length of turn off/turn on operation (a setup) and also provide a sequence of jobs that minimises the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimisation problem that minimises total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP‐hard problem, a new greedy randomised multiobjective adaptive search metaheuristic is utilised to obtain an approximate pareto front (i.e. an approximate set of non‐dominated solutions). Analytical Hierarchy Process is utilised to determine the ‘best’ alternative among the solutions on the pareto front. The proposed framework is illustrated in a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.     

The study of production and inventory policy of manufacturing/remanufacturing environment in a closed-loop supply chain

The life cycle of electronic and electrical equipment has been ruthlessly shortened due to the rapid promotion of new products. Such a phenomenon has thereby resulted in serious environmental pollution and damages to the natural ecology. In order to reduce electronic wastes and increase recycling products, a manufacturing/remanufacturing simulation model was constructed through a series of inventory management policies. The model included the use of the traditional supply chain structures for the forward movement of goods to consumers, as well as a number of specialised operations required to perform reverse supply chain activities. Such a model took into account all the supply chain members such as the suppliers, manufacturers, logistics centres and customers at the same time. Finally, a case study on the application of the model was conducted.     

Climate change and adoption of sustainable land management practices in the Niger basin of Benin

Due to the important role that the agricultural sector plays in sustaining growth and reducing poverty in developing countries, the adoption of practices that have the potential to simultaneously improve agricultural productivity while minimizing environmental impacts is essential. This paper examines the determinants of farmers’ perceptions of climate change and subsequent adoption of sustainable land management practices in the Niger basin of Benin. Binary and multivariate probit models are applied in a two‐stage regression procedure to cross‐sectional data collected through a survey of 545 randomly selected farm households in 28 villages. The findings indicate that there are substitutabilities among three pairs of sustainable land management practices being used by the farmers. Climate change perception is positively related to land tenure, experience in farming, number of relatives, tractor use, and membership in farmers’ organizations, and negatively related to household size, remoteness, and plough use. Moreover, the findings reveal that the uptake of land management practices is related to assets, land tenure, education level of the household head, remoteness, social network, non‐irrigated land size, having a farm located near a river/lake/stream, tractor and plough use, being a subsistence farmer or not, and memberships in farmers’ organizations. The adoption of sustainable land management practices could be encouraged through improving access to markets, adequate roads, and technologies, as well as by promoting membership in farmers’ organizations.     

Factors affecting the adoption of multiple climate‐smart agricultural practices in the Indo‐Gangetic Plains of India

Climate change poses a major threat to agricultural production and food security in India, and climate‐smart agriculture (CSA) is crucial in addressing the potential impacts. Using survey data from 1,267 farm households in 25 villages from Bihar and Haryana in the Indo‐Gangetic Plains, this study analyzes the factors that determine the probability and level of adoption of multiple CSA practices, including seeds of stress‐tolerant varieties, minimum tillage, laser land leveling, site‐specific nutrient management and crop diversification. We applied a multivariate probit model for the simultaneous multiple adoption decisions, and ordered probit models for assessing the factors affecting the level of adoption. The adoption of the various CSA practices is interrelated, whereas several factors, including household characteristics, plot characteristics, market access and major climate risks are found to affect the probability and level of CSA adoption. Climate‐smart agriculture (CSA) adoption and its intensity also vary significantly between eastern Bihar, which is relatively poor and densely populated, and north‐western Haryana. Engaging multiple stakeholders such as farmers, agricultural institutions, agricultural service providers and concerned government departments at the local level is crucial for the large‐scale uptake of CSA. The study, therefore, calls for agricultural policy reforms so that most of the issues related to the uptake of CSA can be adequately addressed.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Reservoir Sedimentation Rates in the Little Washita River Experimental Watershed,Oklahoma: Measurement and Controlling Factors

Forty‐five flood control reservoirs, authorized in the Watershed Protection and Flood Prevention Act 1954, were installed by United States Department of Agriculture (USDA) between 1969 and 1982 in the Little Washita River Experimental Watershed (LWREW), located in central Oklahoma. Over time, these reservoirs have lost sediment and flood storage capacity due to sedimentation, with rates dependent on upstream land use and climate variability. In this study, sedimentation rates for 12 reservoirs representing three major land use categories within LWREW were measured based on bathymetric surveys that used acoustic profiling system. Physiographic and climate attributes of drainage area of surveyed reservoirs were extracted from publicly available data sources including topographic maps, digital elevation models, USDA Natural Resource Conservation Service soils, and weather station databases. Correlation, principal component analysis, and stepwise regression were utilized to analyze the relationship between normalized reservoir sedimentation rates (ReSRa) and the drainage area characteristics to determine the major variables controlling sedimentation within the LWREW. Percent of drainage area with extreme slopes, saturated hydraulic conductivity, and maximum daily rainfall event recorded in spring explained most of the variability in ReSRa. It was also found that percent reduction in reservoir surface area can be used as a surrogate for estimating ReSRa. The implications of the results are discussed.     

Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin,Canada

Gauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.