ENTROPY THEORY SERIES

Дimpfl T, Peter FJ (2013) Using transfer entropy to measure information flows between financial markets. Sci Total Environ 544:330вЂ“338Дaliakopoulos IN, Tsanis IK (2016) Comparison of an artificial neural network and a conceptual rainfallвЂ“runoff model in the simulation of ephemeral streamflow. J Environ Manage 90(11):3675вЂ“3686Дa Silva VdPR, Belo Filho AF, Almeida RSR, de Holanda RM, da Cunha Campos JHB (2016) Shannon information entropy for assessing spaceвЂ“time variability of rainfall and streamflow in semiarid region. Chaos, Solitons Fractals 107:26вЂ“38Д¬ui B, Wang C, Tao W, You Z (2009) River channel network design for drought and flood control: A case study of Xiaoqinghe River basin, Jinan City China. Д¬lemente GP, Grassi R (2018) Directed clustering in weighted networks: A new perspective.

ENTROPY THEORY SERIES

Water 9(11):850Д¬hou C-M (2014) Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Д¬hiaudani A, Di Curzio D, Palmucci W, Pasculli A, Polemio M, Rusi S (2017) Statistical and fractal approaches on long time-series to surface-water/groundwater relationship assessment: A central Italy alluvial plain case study. Environ Earth Sci 74(7):6047вЂ“6063Д«ussi G, Dadson SJ, Prudhomme C, Whitehead PG (2016) Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK). J Hydrol 508:227вЂ“239Д«rown SC, Versace VL, Lester RE, Walter MT (2015) Assessing the impact of drought and forestry on streamflows in south-eastern Australia using a physically based hydrological model. Geophys Res Lett 40(16):4386вЂ“4392Д«ooker D, Woods R (2014) Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments. CES Working Paper: 30 pages.Д«oers N, Bookhagen B, Marwan N, Kurths J, Marengo J (2013) Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System. (01)00548-0Д«illio M, Frattarolo L, Gatfaoui H, De Peretti P (2016) Clustering in dynamic causal networks as a measure of systemic risk on the Euro Zone. Science 286(5439):509вЂ“512Д«euselinck L, Govers G, Hairsine PB, Sander GC, Breynaert M (2002) The influence of rainfall on sediment transport by overland flow over areas of net deposition. J Hydrol 563:802вЂ“810Д«arabГЎsi A-L, Albert R (1999) Emergence of scaling in random networks. Therefore, the proposed methodology can be useful for determining the tendencies of the nodes in a network to make a cluster with strong or weak relationships with other nodes.ДЄgarwal A, Marwan N, Maheswaran R, Merz B, Kurths J (2018) Quantifying the roles of single stations within homogeneous regions using complex network analysis. These stations constitute a low number of triangles (or groups) with other stations but are significantly influential over other stations, especially located in Victoria (in the southeast). However, while the stations in Western Australia have the lowest clustering coefficients, they have also the highest out-strength values among the stations. The stations in Western Australia and Victoria have relatively lower in- and out-clustering values, indicating that these stations have lower tendency to make a cluster with other stations. It can be concluded that while the stations in the northern parts of Australia affect other stations, they are also influenced by others in a reciprocal relationship as shown by the high cyclic-clustering values for these regions. Further, the highest number of out-clustering values are also obtained for the northern parts of Northern Territory and Queensland. The highest number of in-clustering values are obtained for the northern parts of Northern Territory and Queensland in addition to the eastern parts of Queensland and New South Wales. The nature of the network is studied by determining the in-clustering, out-clustering, and cyclic-clustering coefficients. The methodology is applied to a rainfall network of 218 stations across Australia, and total monthly rainfall data observed over the period 1981вЂ“2006 are analysed. This study demonstrates the concept of transfer entropy for the directedвЂ“weighted complex network to study rainfall dynamics, especially to establish the statistically significant information flow between the nodes. In this regard, entropy theory can be useful to better identify the information flow between the nodes. rainfall stations) for some assumed threshold levels to identify possible relationships/links. One of the most critical limitations in such studies is the use of linear correlation between the nodes (e.g. Applications of complex networks-based concepts in hydrology are gaining momentum at the current time.