气候变化领域集成服务门户(CCPortal): Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach

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DOI	10.1088/1748-9326/ab2c26
	Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach
	Sharma S.; Gall H.; Gironás J.; Mejia A.
发表日期	2019
ISSN	17489318
卷号	14 期号:8
英文摘要	Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration's Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1-3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	climate forecast system; ensembles; hydrologic model; subseasonal-to-seasonal forecasting; water quantity/quality forecasting
语种	英语
scopus关键词	Atmospheric movements; Climate models; Flood control; Nutrients; Runoff; Suspended sediments; Water quality; Water supply; Climate forecasts; ensembles; Hydrologic modeling; Seasonal forecasting; Water quantities; Forecasting; climate change; climate prediction; decision making; ensemble forecasting; estuary; flood control; hydrological modeling; hydrometeorology; policy making; seasonality; suspended sediment; tributary; water quality; weather forecasting; Chesapeake Bay; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154463
作者单位	Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, United States; Department of Agricultural and Biological Engineering, Pennsylvania State University, University Park, PA, United States; Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Chile; Centro Nacional de Investigación Para la Gestión Integrada de Desastres Naturales, Chile; Centro de Desarrollo Urbano Sustentable (CEDEUS), Chile; Centro Interdisciplinario de Cambio Global, Pontificia Universidad Católica de Chile, Chile
推荐引用方式 GB/T 7714	Sharma S.,Gall H.,Gironás J.,et al. Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach[J],2019,14(8).
APA	Sharma S.,Gall H.,Gironás J.,&Mejia A..(2019).Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach.Environmental Research Letters,14(8).
MLA	Sharma S.,et al."Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach".Environmental Research Letters 14.8(2019).