TY - JOUR
T1 - Associating Climatic Trends with Stochastic Modelling of Flow Sequences
AU - Patidar, Sandhya
AU - Tanner, Eleanor
AU - Bankaru-Swamy, Soundharajan
AU - Sen Gupta, Bhaskar
N1 - Funding Information:
Funding: This work is funded by GCRF Scottish Government (Heriot-Watt Internal) funded project "Understanding Impacts of EL Niño events on the Indian Agricultural Productivity (UNITE)", 1 April 2019–September 2019.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/6/13
Y1 - 2021/6/13
N2 - 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 evapotran-spiration (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.
AB - 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 evapotran-spiration (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.
KW - Climate change
KW - El Niño/Southern Oscillation (ENSO)
KW - Extreme events modelling
KW - Stochastic modelling
KW - Streamflow
UR - http://www.scopus.com/inward/record.url?scp=85108846800&partnerID=8YFLogxK
U2 - 10.3390/geosciences11060255
DO - 10.3390/geosciences11060255
M3 - Article
SN - 2076-3263
VL - 11
JO - Geosciences
JF - Geosciences
IS - 6
M1 - 255
ER -
