Next Article in Journal
Digital Elevation Models of Rockfalls and Landslides: A Review and Meta-Analysis
Previous Article in Journal
Seismicity and the State of Stress in the Dezful Embayment, Zagros Fold and Thrust Belt
Previous Article in Special Issue
Quantifying Uncertainty in the Modelling Process; Future Extreme Flood Event Projections Across the UK
Article

Associating Climatic Trends with Stochastic Modelling of Flow Sequences

1
The School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, UK
2
Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK
3
Department of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
*
Author to whom correspondence should be addressed.
Academic Editors: Jesus Martinez-Frias and Rui A.P. Perdigão
Geosciences 2021, 11(6), 255; https://doi.org/10.3390/geosciences11060255
Received: 12 May 2021 / Revised: 8 June 2021 / Accepted: 10 June 2021 / Published: 13 June 2021
(This article belongs to the Special Issue Applications of Mathematical/Statistical Techniques to Extreme Events)
Water is essential to all lifeforms including various ecological, geological, hydrological, and climatic processes/activities. With the changing climate, associated El Niño/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across the globe. Changes in P and EV patterns are highly sensitive to temperature (T) variation and thus also affect natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework (HMM_GP) that integrates a hidden Markov model (HMM) with a generalised Pareto (GP) distribution for simulating synthetic flow sequences. The GP distribution within the HMM_GP model aims to improve the model’s efficiency in effectively simulating extreme events. This paper further investigated the potential of generalised extreme value distribution (GEV) coupled with an HMM model within a regression-based scheme for associating the impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic was thoroughly assessed for its suitability to generate and predict synthetic river flow sequences for a set of future climatic projections, specifically during ENSO events. The new modelling schematic can be adapted for a range of applications in hydrology, agriculture, and climate change. View Full-Text
Keywords: stochastic modelling; climate change; streamflow; El Niño/Southern Oscillation (ENSO); extreme events modelling stochastic modelling; climate change; streamflow; El Niño/Southern Oscillation (ENSO); extreme events modelling
Show Figures

Figure 1

MDPI and ACS Style

Patidar, S.; Tanner, E.; Soundharajan, B.-S.; SenGupta, B. Associating Climatic Trends with Stochastic Modelling of Flow Sequences. Geosciences 2021, 11, 255. https://doi.org/10.3390/geosciences11060255

AMA Style

Patidar S, Tanner E, Soundharajan B-S, SenGupta B. Associating Climatic Trends with Stochastic Modelling of Flow Sequences. Geosciences. 2021; 11(6):255. https://doi.org/10.3390/geosciences11060255

Chicago/Turabian Style

Patidar, Sandhya, Eleanor Tanner, Bankaru-Swamy Soundharajan, and Bhaskar SenGupta. 2021. "Associating Climatic Trends with Stochastic Modelling of Flow Sequences" Geosciences 11, no. 6: 255. https://doi.org/10.3390/geosciences11060255

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop