Adjusting for conditional bias in process model simulations of hydrological extremes: an experiment using the North Wyke Farm Platform

A - Papers appearing in refereed journals

Curceac, S., Atkinson, P. M., Milne, A. E., Wu, L. and Harris, P. 2020. Adjusting for conditional bias in process model simulations of hydrological extremes: an experiment using the North Wyke Farm Platform. Frontiers in Artificial Intelligence. 3 (82). https://doi.org/10.3389/frai.2020.565859

AuthorsCurceac, S., Atkinson, P. M., Milne, A. E., Wu, L. and Harris, P.
Abstract

Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model’s output with: (a) a semi-parametric conditional extreme model and (b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, UK. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.

KeywordsPeak flow; Conditional Extreme model; Extreme learning machine; Process-based model (PBM); Hybrid; Grassland agriculture
Year of Publication2020
JournalFrontiers in Artificial Intelligence
Journal citation3 (82)
Digital Object Identifier (DOI)https://doi.org/10.3389/frai.2020.565859
Web address (URL)https://www.frontiersin.org/articles/10.3389/frai.2020.565859/abstract
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
The North Wyke Farm Platform- National Capability [2017-22]
Publisher's version
Accepted author manuscript
Output statusPublished
Publication dates
Online09 Oct 2020
Publication process dates
Accepted17 Sep 2020
ISSN2624-8212

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