2 results
Search Results
Now showing 1 - 2 of 2
- ItemMultiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India(Basel : MDPI, 2021) Kalyan, AVS; Ghose, Dillip Kumar; Thalagapu, Rahul; Guntu, Ravi Kumar; Agarwal, Ankit; Kurths, Jürgen; Rathinasamy, MaheswaranAccelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to ‐understand these events’ propagation in the near fu-ture. We have considered sixteen extreme climate indices defined by the World Meteorological Or-ganization’s Expert Team on Climate Change Detection and Indices from a long‐term dataset (1951– 2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non‐stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain com-binations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indi-cating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16‐year bands, and the two‐year period was dominant in the global power spectrum around 1970–1990. One interest-ing finding is that a dominant two‐year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes. © 2021 by the author. Licensee MDPI, Basel, Switzerland.
- ItemOptimal design of hydrometric station networks based on complex network analysis(Munich : EGU, 2020) Agarwal, Ankit; Marwan, Norbert; Maheswaran, Rathinasamy; Ozturk, Ugur; Kurths, Jürgen; Merz, BrunoHydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure - the weighted degree-betweenness (WDB) measure - to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail © Author(s) 2020.