Managing the socio-ecology of very large rivers: Collective choice rules in IWRM narratives

Integrated water resources management (IWRM) has been lauded as an integrative and participatory form of governance. However, critics claim that actual implementation remains problematic, because of deep path dependencies and the entrenched interests. This paper investigates this claim by looking at the formation of collective choice rules in integrated water resources management reforms in China’s Yellow River and the Ganges in India. The two rivers provide a natural experiment—similarity in physical scale, complexity, and integrated water resources management reforms, but highly different in social and policy contexts. Using the Q methodology and Ostrom’s Institutional Analysis and Development (IAD) framework, we find that, despite differences in policy contexts, narratives amongst the stakeholders in the two rivers are surprisingly similar, including a continued role for a negotiated local approach, and the presence of normative incentives for collective action, underwritten by deep historical meanings of the rivers. These narratives in turn provide some explanation for the choice of collective rules in use. They suggest that a modified form of integrated water resources management, taking into account narratives and collective choice rules, is useful for the governance of very large rivers across different contexts.     

低应力区岩石变形记忆效应形成机制及失忆性

地应力信息储存于开挖后的岩石中,变形率变化法(DRA)是基于岩石变形记忆效应测量地应力的方法,正确的机制是认识变形记忆效应与推广、改进DRA法的基础。传统推测的机制并不能解释低应力区存在变形记忆效应及失忆性等现象。首先进行火山沉积岩试样的物理试验,证明低应力区仍然存在变形记忆效应,记忆信息可同时采用侧向应变及轴向应变测量,两者精度一致。提出在低应力区,岩石变形记忆效应机制为微裂纹接触面及颗粒接触面的摩擦滑动。基于此机制,采用弹性元件、黏性元件和圣维南体构建相应的轴对称理论基本单元模型及多接触面理论模型。进行不同放置时间下的单轴循环压缩试验。结果表明,由轴对称理论模型可以得到低应力区岩石变形记忆效应、侧向DRA曲线与轴向DRA曲线准确度一致且在记忆信息处向上弯曲等、并对失忆性现象进行了初探。理论模型与物理试验结果一致,由此证明微裂纹接触面及颗粒接触面的摩擦滑动可以得到低应力区岩石变形记忆效应。同时,理论模型及物理试验结果为提高DRA法的测量准确度提供了依据。     

瑞利参数在海浪波高机器学习预测中的应用

海浪直接影响海上活动和航行安全,同时也蕴藏着巨大的可再生能源,对海浪核心参数之一波高预测至关重要。基于2015年7月～2022年6月山东小麦岛(36°N,120.6°E)站点实测的波高数据,利用反向传播神经网络(back-propagation neural network,BPNN)、长短记忆网络(long short-term memory, LSTM)和支持向量机回归(support vector regression, SVR)三种机器学习模型对波高进行预测,并分析了瑞利参数的引入对预测结果的影响。结果显示,模型输入项引入瑞利参数后,对1 h和6 h波高预测提升效果有限,预测值与测试集的相关性提升不超过0.02,均方根误差的降低不超过0.01 m;在12h和24h的预测中,BPNN和LSTM模型预测结果相关性提升0.03～0.07,均方根误差降低0.02～0.03m,而SVR模型预测结果变化不显著。说明瑞利参数有助改善BPNN和LSTM模型中长期海浪预报。此外,特征扰动方法(机器学习中特征重要性的计算方法之一)验证了瑞利参数在波高预测中的重要性,瑞利参数的引入为波高的机器学习预...     

分数阶时间导数计算方法在含黏滞流体黏弹双相VTI介质波场模拟中的应用

相对于整数阶导数,分数阶微分算子可以更简洁地描述具有历史依赖性和空间全域相关性的复杂力学和物理过程。但是对分数阶波动方程进行数值模拟,计算量和存储量均较大,尤其对长时间或大计算域的模拟更是如此。文中给出了3种计算方法：全局记忆法、短时记忆法、自适应记忆法,并将这3种方法应用于含黏滞流体黏弹双相VTI （横向各向同性）介质分数阶波传播方程正演。通过对比3种方法的模拟精度、计算时间及占用内存发现：虽然短时记忆法可以通过设置短时记忆长度来调整计算时间与所占内存,但是短时记忆长度越短,精度越差;而自适应记忆法在保证精度的前提下,是短时记忆法与全局记忆法在计算时间与占用内存两方面的折衷。最后对各方法的利弊进行总结,为后续正演模拟及新的分数阶数值算法开发提供方法上的参考。在正演过程中,不仅要使所建模型更贴近实际地下介质,还需对选取的数值算法在计算时间、计算存储量和精度之间进行利弊权衡,以得到一个比较合理的数值算法。     

The influence of neoliberalization on the success and failure of fishing cooperatives in contemporary small-scale fishing communities: A case study from Yucatán,Mexico

This paper examines the effects of neoliberalization on the opportunities and constraints that fishing cooperatives face in Yucatán, Mexico. Cooperatives have the potential to enhance small-scale fishing livelihoods and participate in sustainable resource governance. However, promoting cooperatives’ success entails developing a realistic understanding of the political and economic contexts in which they operate. Drawing on interview and census data, the analysis employs the theory of club goods to examine how the neoliberalization of Mexican fisheries policies in the 1980s and 1990s has affected cooperatives’ ability to provide members with collective benefits, and thus the success and failure of fishing cooperatives in the region. In general, neoliberalization has reduced support to fishing cooperatives and generated greater challenges for their success in Yucatán. The results of this study are likely relevant to many other small-scale fisheries in the South that have undergone similar processes of neoliberalization.     

自升式海洋平台关键部位MMM与ACFM联合检测

针对传统无损检测方法对自升式海洋平台检测工作量大、费时费力等问题,提出了一种金属磁记忆(MMM)与交流电磁场(ACFM)联合检测的新方法。在简介MMM和ACFM检测机理之后,将其应用到自升式海洋平台关键部位的无损检测中。分别以齿条座板与桩腿之间的T型焊缝和桩腿环焊缝为例,首先通过MMM快速全面扫描待检测表面,基于磁场分布及梯度值确定应力集中部位;在此基础上,利用ACFM方法重点对应力集中部位进行裂纹缺陷的定量化检测,结果表明MMM对应力集中或微观缺陷非常敏感,而ACFM方法可精确给出裂纹缺陷的深度信息,为平台结构的安全可靠运行和维修方案制定提供了方法参考和理论依据。     

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.     

食物景观对地方社会记忆的表征和塑造 ——以《舌尖上的中国》为例

以纪录片《舌尖上的中国》为数据来源,借助情感挖掘、语义网络分析以及扎根理论等方法探究食物景观对社会记忆的表征内容和塑造过程.研究结果表明:1)从情感分析结果来看,个体对食物景观的情感倾向总体呈现积极趋势,不同年龄、复杂的社会经历以及多元的社会角色是影响个体情感变化的重要因素,人们的情感认同形成了社会主流意识形态,并框定...     

基于小波变换和LSTM神经网络的格陵兰岛近海海域海平面变化预测

海平面变化包含多种不同时间尺度信息，传统的预测方法仅对海平面变化趋势项、周期项进行拟合，难以利用海平面变化的不同时间尺度信号，使得预测精度不高。本文基于深度学习的预测模型，提出一种融合小波变换（wavelet transform，WT）与LSTM （long short-term memory，LSTM）神经网络的海平面异常组合预测模型。首先利用小波分解得到反映海平面变化总体趋势的低频分量和刻画主要细节信息的高频分量；然后通过LSTM神经网络对代表不同时间尺度的各个分量预测和重构，实现海平面变化的非线性预测。基于该模型的海平面变化预测的均方根误差、平均绝对误差和相关系数分别为12.76 mm、9.94 mm和0.937，预测精度均优于LSTM和EEMD-LSTM预测模型，WT-LSTM组合模型对区域海平面变化预测具有较好的应用价值。     

基于海洋气候数据集的区域海平面变化非线性预测

本文基于中国首套长时间序列、高精度、高时空一致性的全球海洋气候数据集产品, 利用1993年1月至2015年12月的山东半岛近海海平面异常数据, 构建了基于集合经验模式分解(EEMD)和长短期记忆神经网络(LSTM)的海平面非线性变化组合预测模型。EEMD可以得到海平面异常的各周期项、线性趋势及残差部分, LSTM模型可对其进行逐个预测并重构得到最终的海平面异常预测结果。EEMD-LSTM组合模型海平面异常预测的均方根误差仅为25.87 mm, 取得了令人满意的效果。基于该组合模型预测2016-2025年山东半岛近海海平面上升速率将达到3.54 mm·a^-1。