応用水文 = Applied hydrology

Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original algorithm for regression and classification problems, while numerous developments could be also useful in solving diverse practical problems in the water sector. Here we popularize RF and their variants for the practicing water scientist, and discuss related concepts and techniques, which have received less attention from the water science and hydrologic communities. In doing so, we review RF applications in water resources, highlight the potential of the original algorithm and its variants, and assess the degree of RF exploitation in a diverse range of applications. Relevant implementations of random forests, as well as related concepts and techniques in the R programming language, are also covered.

https://www.mdpi.com/2073-4441/11/5/910/pdf

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

Renewable energy sources and climate change mitigation

AbstractThe authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-ter...

Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP6 Twentieth-Century Climate Simulations

Income distribution as a central topic in development has waxed and waned over the last half-century. In the immediate post-Second World War period, income distribution was seen as central, linked to such issues as the need for land distribution and the generation of a surplus by transferring underused labour from rural to urban areas, as outlined in the theory of Arthur Lewis. Income distribution then faded as an issue until the 1970s, when priorities for generating employment and meeting basic needs were often seen as requiring macro strategies of redistribution with growth. In the 1980s and 1990s, income distribution faded once more as attention shifted to policies of economic stabilisation and structural adjustment. By the early twenty-first century, income distribution became once again an important point of focus, both for policy and analysis. This was stimulated in part by concerns that globalisation was creating forces in many countries that were exacerbating greater inequalities of income distribution, which required positive corrective action. Concerns with income distribution in development reflect values and objectives, theory and analysis. The links with values and objectives are readily apparent – although it is important to stress that attention to income distribution goes well beyond concern with poverty, both conceptually and analytically. A focus on income distribution encompasses issues of equity, justice and power. Theory and analysis exercise their own important influence on perceptions and policy. Neoclassical economic theory tends to treat income distribution as an outcome and to direct attention to policy issues of price and the efficient allocation of resources. In contrast, structural analysis, neo-Marxian analysis and, more recently, human development analysis give much greater place to income distribution as a causal factor, both in understanding and modelling the process of development and in identifying crucial variables influencing outcomes. Accordingly, within these three paradigms , income distribution takes on an importance in setting the national objectives of development and in developing strategy within the political–economic parameters of the situation. Empirical research has uncovered other relationships. There is evidence, for example, that inequality within countries can inhibit economic growth and slow poverty reduction. It can have repercussions on civic, social and political life and institutions, undermining social solidarity. And there is evidence that social stress in more unequal societies is associated with lower levels of health for all social classes, not merely the poorest. Research has also shown that inequality between geographic, ethnic or social groups …

소득분배와 사회복지(Income Distribution)

To seek stochastic analogies in key processes related to the hydrological cycle, an extended collection of several billions of data values from hundred thousands of worldwide stations is used in this work. The examined processes are the near-surface hourly temperature, dew point, relative humidity, sea level pressure, and atmospheric wind speed, as well as the hourly/daily streamflow and precipitation. Through the use of robust stochastic metrics such as the K-moments and a second-order climacogram (i.e., variance of the averaged process vs. scale), it is found that several stochastic similarities exist in both the marginal structure, in terms of the first four moments, and in the second-order dependence structure. Stochastic similarities are also detected among the examined processes, forming a specific hierarchy among their marginal and dependence structures, similar to the one in the hydrological cycle. Finally, similarities are also traced to the isotropic and nearly Gaussian turbulence, as analyzed through extensive lab recordings of grid turbulence and of turbulent buoyant jet along the axis, which resembles the turbulent shear and buoyant regime that dominates and drives the hydrological-cycle processes in the boundary layer. The results are found to be consistent with other studies in literature such as solar radiation, ocean waves, and evaporation, and they can be also justified by the principle of maximum entropy. Therefore, they allow for the development of a universal stochastic view of the hydrological-cycle under the Hurst–Kolmogorov dynamics, with marginal structures extending from nearly Gaussian to Pareto-type tail behavior, and with dependence structures exhibiting roughness (fractal) behavior at small scales, long-term persistence at large scales, and a transient behavior at intermediate scales.

A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes

Revisiting long-range dependence in annual precipitation

On the long-range dependence properties of annual precipitation using a global network of instrumental measurements

Clustering of extremes is critical for hydrological design and risk management and challenges the popular assumption of independence of extremes. We investigate the links between clustering of extr...

Revealing hidden persistence in maximum rainfall records

In February 2017, a failure occurring in Oroville Dam’s main spillway risked causing severe damages downstream. A unique aspect of this incident was the fact that it happened during a flood scenario well within its design and operational procedures, prompting research into its causes and determining methods to prevent similar events from reoccurring. In this study, a hydroclimatic analysis of Oroville Dam’s catchment is conducted, along with a review of related design and operational manuals. The data available allows for the comparison of older flood-frequency analyses to new alternative methods proposed in this paper and relevant literature. Based on summary characteristics of the 2017 floods, possible causes of the incident are outlined, in order to understand which factors contributed more significantly. It turns out that the event was most likely the result of a structural problem in the dam’s main spillway and detrimental geological conditions, but analysis of surface level data also reveals operational issues that were not present during previous larger floods, promoting a discussion about flood control design methods, specifications, and dam inspection procedures, and how these can be improved to prevent a similar event from occurring in the future.

https://www.mdpi.com/2076-3263/9/1/37/pdf

Theano Iliopoulou

Papers

A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes

Revisiting long-range dependence in annual precipitation

On the long-range dependence properties of annual precipitation using a global network of instrumental measurements

Revealing hidden persistence in maximum rainfall records

Insights into the Oroville Dam 2017 Spillway Incident