Multiple model combination methods for annual maximum water level prediction during river ice breakup

The Athabasca River is the largest unregulated river in Alberta, Canada, with ice jams frequently occurring in the vicinity of Fort McMurray. Modelling tools are desired to forecast ice‐related flood events. Multiple model combination methods can often obtain better predictive performances than any member models due to possible variance reduction of forecast errors or correction of biases. However, few applications of this method to river ice forecasting are reported. Thus, a framework of multiple model combination methods for maximum breakup water level (MBWL) Prediction during river ice breakup is proposed. Within the framework, the member models describe the relations between the MBWL (predicted variable) and their corresponding indicators (predictor variables); the combining models link the relations between the predicted MBWL by each member model and the observed MBWL. Especially, adaptive neuro‐fuzzy inference systems, artificial neural networks, and multiple linear regression are not only employed as member models but also as combining models. Simple average methods (SAM) are selected as the basic combining model due to simple calculations. In the SAM, an equal weight (1/n) is assigned to n member models. The historical breakup data of the Athabasca River at Fort McMurray for the past 36 years (1980 to 2015) are collected to facilitate the comparison of models. These models are examined using the leave‐one‐out cross validation and the holdout validation methods. A SAM, which is the average output from three optimal member models, is selected as the best model as it has the optimal validation performance (lowest average squared errors). In terms of lowest average squared errors, the SAM improves upon the optimal artificial neural networks, adaptive neuro‐fuzzy inference systems, and multiple linear regression member models by 21.95%, 30.97%, and 24.03%, respectively. This result sheds light on the effectiveness of combining different forecasting models when a scarce river ice data set is investigated. The indicators included in the SAM may indicate that the MBWL is affected by water flow conditions just after freeze‐up, overall freezing conditions during winter, and snowpack conditions before breakup.     

Resilience for disaster risk management in a changing climate: Practitioners’ frames and practices

There is a growing use of resilience ideas within the disaster risk management literature and policy domain. However, few empirical studies have focused on how resilience ideas are conceptualized by practitioners, as they implement them in practice. Using Hajer's ‘social-interactive discourse theory’ this research contributes to the understanding of how practitioners frame, construct and make sense of resilience ideas in the context of changes in institutional arrangements for disaster risk management that explicitly include the resilience approach and climate change considerations. The case study involved the roll out of the Natural Disaster Resilience Program in Queensland, Australia, and the study involved three sites in Queensland. The methods used were observation of different activities and the physical sites, revision of documents related to the Natural Disaster Resilience Program and in-depth semi-structured interviews with key informants, all practitioners who had direct interaction with the program. The research findings show that practitioners construct the meaning of disaster resilience differently, and these are embedded in diverse storylines. Within these storylines, practitioners gave different interpretations and emphasis to the seven discourse categories that characterized their resilience discourse. Self-reliance emerged as one of the paramount discourse categories but we argue that caution needs to be used when promoting values of self-reliance. If the policy impetus is a focus on learning, research findings indicate it is also pertinent to move from experiential learning toward social learning. The results presented in this study provide helpful insights to inform policy design and implementation of resilience ideas in disaster risk management and climate change, and to inform theory.     

Adjusting wavelet‐based multiresolution analysis boundary conditions for long‐term streamflow forecasting

We propose a novel technique for improving a long‐term multi‐step‐ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks‐based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd.     

Effects of Social Learning on Rural Farmers’ Adaptive Capacity: Empirical Insights from the Vietnamese Mekong Delta

Abstract

Environmental challenges in the Vietnamese Mekong Delta characterized by adverse impacts of climate change, upstream hydropower development and localized dyke expansion present imperatives for rural farmers to “learn to adapt.” However, little is known about how learning contributes to improving their capacity in adapting to these “wicked” problems. This study investigates potential effects of farmers’ learning on their adaptive capacity, utilizing nine focus group discussions, 33 interviews, and a structured survey of 300 farmers. The exploratory factor analysis produced two factors for social learning: (1) learning through social interactions and (2) self-reflection, and one factor for adaptive capacity. The regression results show that the social learning factors have significantly positive effects on adaptive capacity. Farmers with a higher level of social learning are likely to demonstrate higher adaptive capacity. The findings call for policy considerations to promote learning in a broader context of the delta to enhance local capacity.     

Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas

A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R² above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.     

Deep learning approaches for improving prediction of daily stream temperature in data-scarce,unmonitored, and dammed basins

Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (T_s) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling T_s and it is uncertain how an LSTM-based daily T_s model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with and without major dams, and studied how to assemble suitable training datasets for predictions in basins with or without temperature monitoring. For prediction in unmonitored basins (PUB), LSTM produced a root-mean-square error (RMSE) of 1.129°C and an R² of 0.983. While these metrics declined from LSTM's temporal prediction performance, they far surpassed traditional models' PUB values, and were competitive with traditional models' temporal prediction on calibrated sites. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.202°C and an R² of 0.984. For temporal prediction, the most suitable training set was the matching DAG that the basin could be grouped into (for example, the 60% DAG was most suitable for a basin with 61% data availability). However, for PUB, a training dataset including all basins with data was consistently preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results indicate there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations can still be predicted well, and LSTM appears to be a highly accurate T_s modelling tool even for spatial extrapolation.     

Key characteristics of successful fisheries learning exchanges

This study examines the key characteristics of successful fisheries learning exchanges (FLEs). FLEs are peer-to-peer gatherings in which fishery stakeholders from different communities freely exchange information and experiences surrounding fisheries challenges and solutions. They are usually organized by fishers, non-governmental organizations and governments and are credited as an integral tool for the diffusion and adoption of fisheries management strategies. Despite their numerous perceived benefits within fisheries conservation and management, little research has been conducted on FLEs. This multiple case study addressed the research question: “What are the key characteristics of successful FLEs?” Success metrics were defined during a workshop on FLEs in 2013. For this study, the authors selected six successful FLEs that were presented during the workshop. Documentation of FLEs and key informant interviews with participants and organizers were used as data. The following key elements of successful FLEs emerged from analyses: (1) a clear guiding purpose and flexible objectives, (2) careful and considered selection of participants with diverse professions and conservation beliefs, (3) a mix of activities including giving presentations, conducting site visits, talking with local fishers, spending time on boats or in the water, and participating in cultural activities, and (4) logistical and financial follow-up support, including information dissemination about what participants learned at the FLE. Based on these results, the authors provide recommendations for conducting successful FLEs.     

基于自主学习的地图学PBL教学方法研究

研究在地图学课程教学过程中,将传统教学方法和PBL教学方法相结合,设计以任务为驱动的自主学习环节,并通过实证分析学牛对不同教学方法的接受程度、在PBL教学方法中所存在问题以及适宜于PBL教学模式的考评方式.研究表明,地图学教学应改变原有的教学模式,在传统教学模式的基础上,结合PBL教学方法逐步开展.     

Transnational learning, policy analytical capacity, and environmental policy convergence: Survey results from Canada

In this article, we seek to clarify further the effects of internationalization on environmental policy convergence by focussing on a country's policy analytical capacity as a mechanism mediating transnational policy learning. We argue that without significant policy analytical capacity, it is unlikely for transnational communication to produce policy learning crucial to this potential mechanism of international environmental policy convergence. Based on a survey of Canadian provincial public servants, we find that while policy analysts in the environmental policy sector have some interaction with those outside of their own jurisdictions, their particular training, employment patterns, and work activities mean they are unlikely to use knowledge drawn from external sources in their decision-making processes.     

GIS专业任务驱动型实验教学体系的设计

＂任务驱动型＂教学方法是探究式教学模式下的一种教学方法,地理信息系统专业的实验课程由于课程自身的特点更注重学生对实验教学的操作、应用能力的培养,因此非常适合采用＂任务驱动型＂教学方法。在分析了地理信息系统专业目前实验教学存在的问题的基础上,介绍了＂任务驱动型＂的教学思想,设计了对地理信息系统专业的实验教学采取＂任务驱动...