气候变化领域集成服务门户(CCPortal): A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed

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DOI	10.1007/s00382-017-3794-7
	A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed
	Slater L.J.; Villarini G.; Bradley A.A.; Vecchi G.A.
发表日期	2019
ISSN	0930-7575
起始页码	7429
结束页码	7445
卷号	53 期号:12
英文摘要	The state of Iowa in the US Midwest is regularly affected by major floods and has seen a notable increase in agricultural land cover over the twentieth century. We present a novel statistical-dynamical approach for probabilistic seasonal streamflow forecasting using land cover and General Circulation Model (GCM) precipitation forecasts. Low to high flows are modelled and forecast for the Raccoon River at Van Meter, a 8900 km2 catchment located in central-western Iowa. Statistical model fits for each streamflow quantile (from seasonal minimum to maximum; predictands) are based on observed basin-averaged total seasonal precipitation, annual row crop (corn and soybean) production acreage, and observed precipitation from the month preceding each season (to characterize antecedent wetness conditions) (predictors). Model fits improve when including agricultural land cover and antecedent precipitation as predictors, as opposed to just precipitation. Using the dynamically-updated relationship between predictand and predictors every year, forecasts are computed from 1 to 10 months ahead of every season based on annual row crop acreage from the previous year (persistence forecast) and the monthly precipitation forecasts from eight GCMs of the North American Multi-Model Ensemble (NMME). The skill of our forecast streamflow is assessed in deterministic and probabilistic terms for all initialization months, flow quantiles, and seasons. Overall, the system produces relatively skillful streamflow forecasts from low to high flows, but the skill does not decrease uniformly with initialization time, suggesting that improvements can be gained by using different predictors for specific seasons and flow quantiles. © 2017, The Author(s).
英文关键词	North-American Multi Model ensemble (NMME); Probabilistic forecast; Seasonal forecasting; Streamflow forecasts
语种	英语
scopus关键词	flood forecasting; general circulation model; precipitation (climatology); probability; seasonal variation; streamflow; watershed; Iowa; Raccoon River; United States; Glycine max; Procyon; Zea mays
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145835
作者单位	IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA, United States; Department of Geography, Loughborough University, Loughborough, United Kingdom; NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Department of Geosciences, Princeton University, Princeton, NJ, United States; Princeton Environmental Institute, Princeton University, Princeton, NJ, United States
推荐引用方式 GB/T 7714	Slater L.J.,Villarini G.,Bradley A.A.,et al. A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed[J],2019,53(12).
APA	Slater L.J.,Villarini G.,Bradley A.A.,&Vecchi G.A..(2019).A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed.Climate Dynamics,53(12).
MLA	Slater L.J.,et al."A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed".Climate Dynamics 53.12(2019).