Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab2c26 |
Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach | |
Sharma, Sanjib1; Gall, Heather2; Gironas, Jorge3,4,5,6; Mejia, Alfonso1 | |
2019-08-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2019 |
卷号 | 14期号:8 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Chile |
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. |
英文关键词 | ensembles subseasonal-to-seasonal forecasting water quantity/quality forecasting hydrologic model climate forecast system |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000478753700003 |
WOS关键词 | EXTENDED LOGISTIC-REGRESSION ; SUSQUEHANNA RIVER-BASIN ; PRECIPITATION ; PREDICTION ; SYSTEM ; SKILL ; VARIABILITY ; TIME ; MANAGEMENT ; HYDROLOGY |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/185603 |
专题 | 气候变化 |
作者单位 | 1.Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA; 2.Penn State Univ, Dept Agr & Biol Engn, University Pk, PA 16802 USA; 3.Pontificia Univ Catolica Chile, Dept Ingn Hidraul & Ambiental, Santiago, Chile; 4.Ctr Nacl Invest Gest Integrada Desastres Nat CIGI, Santiago, Chile; 5.Ctr Desarrollo Urbano Sustentable CEDEUS, Providencia, Chile; 6.Pontificia Univ Catolica Chile, Ctr Interdisciplinario Cambio Global, Santiago, Chile |
推荐引用方式 GB/T 7714 | Sharma, Sanjib,Gall, Heather,Gironas, Jorge,et al. Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach[J]. ENVIRONMENTAL RESEARCH LETTERS,2019,14(8). |
APA | Sharma, Sanjib,Gall, Heather,Gironas, Jorge,&Mejia, Alfonso.(2019).Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach.ENVIRONMENTAL RESEARCH LETTERS,14(8). |
MLA | Sharma, Sanjib,et al."Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach".ENVIRONMENTAL RESEARCH LETTERS 14.8(2019). |
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