干旱区生态环境知识资源中心(Arid): Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high-altitude regions

Arid

DOI	10.1002/hyp.15046
	Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high-altitude regions
	Jorquera, Joaquin; Pizarro, Alonso
通讯作者	Pizarro, A
来源期刊	HYDROLOGICAL PROCESSES
ISSN	0885-6087
EISSN	1099-1085
出版年	2023
卷号	37 期号:12
英文摘要	Runoff prediction is crucial for effective water resource management and risk mitigation. However, predicting these catchment responses is challenging due to their unique characteristics and the randomness of hydrological processes. This manuscript explores two different types of modelling frameworks (deterministic and stochastic) and aims to answer questions regarding the reliance on stochastic simulation based on deterministic simulations, the suitability of simple deterministic models, and the influence of catchment characteristics on the results. A simple deterministic rainfall-runoff model (with only one model parameter) was used to feed the Brisk Local Uncertainty Estimator for Hydrological Simulations and Predictions (Bluecat) framework, exploring the whole range of values of the model parameter. Our findings showed that Bluecat enhanced the Kling-Gupta Efficiency (KGE) outcomes in arid and semi-arid regions as well as high-altitude catchments. Additionally, using the mean of the confidence band of the stochastic simulation as the simulated discharge, rather than the median, resulted in improved KGE values for all catchments. Hysteresis between S-KGE (stochastic KGE) and D-KGE (deterministic KGE) was observed, indicating a non-monotonic relationship between the two variables, and therefore, S-KGE optimisation can be achieved even when D-KGE is not optimal. Bluecat showed exceptional performance extended to arid and semi-arid regions, as well as high-altitude areas, making it a promising alternative for rainfall-runoff simulations in these challenging locations. We updated an extremely simple deterministic rainfall-runoff model (within the Bluecat framework) to have stochastic hydrological responses in 99 catchments in Chile. Different metrics, hydrological signatures, and uncertainty were computed. The stochastic modelling presented a considerable improvement compared to deterministic modelling, particularly in arid and semi-arid regions and high-altitude areas.image
英文关键词	Bluecat hydrological modelling rainfall-runoff modelling stochastics versus determinism
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001129334600001
WOS关键词	PARAMETER UNCERTAINTY ; BAYESIAN-ESTIMATION ; MODULAR ASSESSMENT ; HESS-OPINIONS ; MODEL ; WATER ; CATCHMENT ; EVOLUTION ; EQUIFINALITY ; ALGORITHM
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396878
推荐引用方式 GB/T 7714	Jorquera, Joaquin,Pizarro, Alonso. Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high-altitude regions[J],2023,37(12).
APA	Jorquera, Joaquin,&Pizarro, Alonso.(2023).Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high-altitude regions.HYDROLOGICAL PROCESSES,37(12).
MLA	Jorquera, Joaquin,et al."Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high-altitude regions".HYDROLOGICAL PROCESSES 37.12(2023).