Identification Of Homogeneous Regions Research Articles

Updating catastrophe models to today’s climate – An application of a large ensemble approach to extreme rainfall

As climate change unfolds, extreme weather events and natural hazards relevant for the insurance industry may change in frequency and intensity. In order to guarantee risk-adequate pricing, risk modelers have to ask themselves the question whether a certain ‘hazard’ parameter based on past observations and calibrated on past data is still valid or needs to be updated. Using the example of heavy rainfall between 3 h and 72 h as a proxy for flash flooding, we apply a unique high-resolution single model initial-condition large ensemble. We outline a methodology by which risk modelers can assess whether, where and by how much updating the hazard component of their risk model is needed. Therefore, we compare two time periods: 1980–1999 (as an example for a typical baseline period) and 2015–2034 (representing a 20-year period centered around today). We argue that assessing changes over the full ensemble space is vital for (i) the identification of homogeneous regions where a certain signal emerges, and (ii) the quantification of risk changes in the tail of the extreme value distribution that may still be hidden in the mean response to climate change. In the example case of 3-hourly 50-year rainfall return levels, we find a significant increase between 1980 and 1999 and 2015–2034 over 44 % of European land area. We also identify specific risk regions such as northwestern Spain where changes in the very tail (100-year rainfall return level) already emerge before more common extremes (1-year rainfall return level). Failing to detect and consider these tail changes or assuming equal changes for common and rare extremes may therefore lead to an under- or overestimation of the true level of risk today.

Regional Flood Frequency Analysis: A Bibliometric Overview

In water resources management, environmental and ecological studies, estimation of design streamflow is often needed. For gauged catchments, at-site flood frequency analysis is used for this purpose; however, for ungauged catchments, regional flood frequency analysis (RFFA) is the preferred method. RFFA attempts to transfer flood characteristics from gauged to ungauged catchments based on the assumption of regional homogeneity. A bibliometric analysis on RFFA is presented here using Web of Science (WoS) and Scopus databases. A total of 626 articles were selected from these databases. From the bibliometric analysis, it has been found that Journal of Hydrology and Water Resources Research are the two leading journals reporting RFFA research. In RFFA research, leading countries include Canada, USA, UK, Italy and Australia. In terms of citations, the top performing researchers are Ouarda T, Burn D, Rahman A, Haddad K and Chebana F. Future research should be directed towards the identification of homogeneous regions, application of efficient artificial intelligence (AI)-based RFFA models, incorporation of climate change impacts and uncertainty analysis.

Identification of homogeneous regions in regional flood frequency analysis under changing climate

Identification of homogeneous regions in regional flood frequency analysis under changing climate

Identification of homogeneous regions in terms of flood seasonality using a complex network approach

Catchment classification aids in the identification of homogeneous regions in which catchments have similar flood timing and corresponding climatic drivers. As a new classification scheme from complex network theory, the community detection method is introduced to classify 242 catchments in the United States based on flood seasonality. The robustness of this method is tested by calculating the Adjusted Rank Index between the classification results from different random subsets of the 242 catchments and different flood sampling methods. In addition, three network metrics (network density, centrality, and k-core nucleus) are used to further unravel the hydrological connections within each community of catchments. Catchments with similar flood seasonality cluster into six large communities, while catchments in sparsely gauged areas or with unique physiographic properties are isolated from large communities. High values of the Adjusted Rank Index show the robustness of the community detection method for catchment classification. The results indicate that the complex network is valid for use in classifying catchments based on flood seasonality. Moreover, for each community, the network metrics are potential descriptors of: (a) the degree of homogeneity, (b) the representativeness of any catchment, and (c) the spatial scale of synchronized floods. Therefore, the complex network is able to characterize the interconnections between catchments within the community.

Adaptation of the L-Moments Method for the Regionalization for Maximum Annual Temperatures in Colombia

<p>The current study aims at developing a regional frequency analysis for Maximum Annual Temperature (MAT) in the hydrographic basins of Colombia. The methodology that was implemented for the regionalization is the <em>L-Moments</em>, proposed by Hosking (1990). The work was performed in five stages: data analysis, computing of <em>L-Moments</em>, identification of homogeneous regions, selection of probability and distribution of the density function and estimation of quantities, and finally the construction of regionalized maps. Overall, fifteen (15) homogeneous regions were identified and selected for annual maximum temperature, which meet certain criteria of homogeneity and discordance. Several distributions for regional frequency analysis were tested in order to select the best probability function: the Z<sup>DIST</sup> statistic was used, considering that the absolute value must be smaller than 1,64. Finally, temperature maps were generated for different return periods. Using the <em>L-Moments</em> methodology, the regionalization procedure was done using the annual average maximum temperature as the key scale parameter. However, the regionalization procedure was done using the geographic location; the physical parameters by gauging stations (Wallis, 2007), with stations located in the same catchment, and considering the coherent-basins approach.</p>

Using entropy theory to improve the definition of homogeneous regions in the semi-arid region of Brazil

ABSTRACTAlthough the semi-arid region of Brazil appears to be homogeneous due to drought conditions, there is a great deal of variability in climatic elements in the region, so that the definition of homogeneous regions will provide the deployment of measures appropriate for each locality. However, the limited information on climatic parameters in the region makes it difficult to define these regions. This problem can, however, be alleviated by the use of entropy theory. Therefore, this study aimed to investigate the potential of the theory to identify hydrologically homogeneous regions for conditions of the semi-arid region of Brazil. Entropy-based Disorder Index (DI) data were computed, based on monthly precipitation and monthly water balance (precipitation – reference evapotranspiration) for 290 gauge stations. For defining homogeneous regions, cluster analysis was utilized, using the data on geographical information about rain gauges (latitude and longitude), annual precipitation, annual water balance, coefficient of skewness, coefficient of kurtosis and DI. The identification of homogeneous regions in the Brazilian semi-arid region was only possible when the grouping of stations was performed, based on DI for precipitation and latitude. Results showed the definition of seven homogeneous regions in the semi-arid region of Brazil.

Identification of homogenous regions in rain-fed watershed using Kohonen neural networks

Identification of homogenous regions and their ranking is important to formulate appropriate strategies for suitable conservation and management practices within the watershed. In this study, Kohonen neural network (KNN) is employed to classify the micro-watersheds of Kaddam watershed in middle Godavari basin (India) into homogeneous groups. KNN algorithm learns to cluster groups of similar input patterns from a high-dimensional input space in a non-linear fashion onto a low-dimensional layer of neurons. Ten geo-morphological parameters are used for classification of micro-watersheds. An optimal number of groups is chosen based on two cluster validation measures, the Davies–Bouldin Index and the Dunn’s Index. By using the KNN method, 18 micro-watersheds are grouped into five homogenous groups based on selected watershed parameters. The clustering results showed concurrence with general ranking of micro-watersheds. Further, the KNN micro-watershed classification results are validated by comparing with the results of K-means clustering algorithm (KCA). From the comparative analysis, it is observed that both the algorithms give approximately similar kinds of classification of micro-watersheds. The obtained results help in identifying the groups of micro-watersheds that should be given top priority (i.e. those that require immediate conservation measures). The study suggests that identifying homogeneous regions can be helpful for effective planning and management of watersheds, and KNN can be applied effectively for micro-watershed zonation.

Learning spatial relations for object-specific segmentation using Bayesian network model

In computer vision tasks such as, for example, object recognition, semantically accurate segmentation of a particular object of interest (OOI) is a critical step. Due to the OOI consisting of visually different fragments, traditional segmentation algorithms that are based on the identification of homogeneous regions usually do not perform well. In order to narrow this gap between low-level visual features and high-level semantics, some recent methods employ machine learning to generate more accurate models of the OOI. The main contribution of this paper is the inclusion of spatial relationships among the OOI fragments into the model. For this purpose, we employ Bayesian networks as a probabilistic approach for learning the spatial relationships which, in turn, becomes evidence that is used for the process of segmenting future instances of the OOI. The algorithm presented in this paper also uses multiple instance learning to obtain prototypical descriptions of each fragment of the OOI based on low-level visual features. The experimental results on both artificial and real image datasets indicate that the addition of spatial relationships improves segmentation performance.

Classification of Annual Precipitations and Identification of Homogeneous Regions using K-Means Method

Reliable and correct estimation of hydrological and meteorological processes is one of the major problems in regions with insufficient hydrologic information and data. The classification of the hydrological variables and determination of homogeneous regions are the most important steps of regional studies. The purpose of this study is to classify the annual total precipitation series and to identify the homogeneous regions by K-Means method. The K-means method, which is the simplest and most commonly used clustering method, divides a data set into clusters by minimizing the sum of the Euclidean distance between each feature vector and its closest cluster centre. The annual precipitation records and longitude, latitude and altitude values obtained of 188 stations operated by the National Meteorology Works (DMI) in Turkey were considered for clustering analysis. The number of clusters was determined as 7. Moreover, the regional homogeneity test was applied for testing the homogeneity of regions identified by clustering analysis.

Development of an Objective Procedure for Identifying Regions of Low Flow Homogeneity for the Specification of Environmental Flows

This paper describes the developn1ent of a procedure for the identification of regions of low flow homogeneity for the operational specification of environmental flows. The objective of the procedure is to assign ungauged catchments to a small number of “indicator” streamflow gauges that can be considered to behave in a similar manner during low flow conditions. The identification of homogeneous regions involved the selection of appropriate candidate variables suited to characterising catchment similarity. The allocation of ungauged catchments to indicator stations was achieved by reducing the selected variables down to two factors using principal component analysis. A classification tree was then formulated to enable regional group membership to be determined. The allocation method provides an objective basis for determining the most appropriate indicator gauging station for an ungauged catchment, and also enables the definition of homogeneous regions to be determined.

A method for delineating structurally homogeneous regions in protein sequences.

A homogeneous region in a protein sequence is a set of contiguous residues that share common features, concerning physico-chemical, structural and mutational information. This paper presents a method for identifying such homogeneous regions. From a profile describing a given type of biological information along the sequence, the algorithm allows the segmentation of the sequence by optimizing a criterion characterized by two user-defined control parameters: the 'homogenizing degree' of the regions and the 'site neighbourhood' size. We apply the method to the envelope proteins of the human immunodeficiency virus HIV-1, for the identification of homogeneous regions in a hydrophobicity profile and the delineation of variable and conserved regions in a variability profile.