GSTDTAP
DOI10.1007/s00382-017-3794-7
A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed
Slater, Louise J.1,2; Villarini, Gabriele1; Bradley, A. Allen1; Vecchi, Gabriel A.3,4,5
2019-12-01
发表期刊CLIMATE DYNAMICS
ISSN0930-7575
EISSN1432-0894
出版年2019
卷号53期号:12页码:7429-7445
文章类型Article
语种英语
国家USA; England
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 km(2) 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.


英文关键词Seasonal forecasting Probabilistic forecast Streamflow forecasts North-American Multi Model ensemble (NMME)
领域气候变化
收录类别SCI-E
WOS记录号WOS:000495247200019
WOS关键词MISSISSIPPI RIVER ; CLIMATE ; PREDICTION ; MODELS ; PREDICTABILITY ; WEATHER ; SKILL
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向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/224301
专题环境与发展全球科技态势
作者单位1.Univ Iowa, IIHR Hydrosci & Engn, Iowa City, IA 52242 USA;
2.Loughborough Univ, Dept Geog, Loughborough, Leics, England;
3.NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA;
4.Princeton Univ, Dept Geosci, Princeton, NJ 08544 USA;
5.Princeton Univ, Princeton Environm Inst, Princeton, NJ 08544 USA
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GB/T 7714
Slater, Louise J.,Villarini, Gabriele,Bradley, A. Allen,et al. A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed[J]. CLIMATE DYNAMICS,2019,53(12):7429-7445.
APA Slater, Louise J.,Villarini, Gabriele,Bradley, A. Allen,&Vecchi, Gabriel A..(2019).A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed.CLIMATE DYNAMICS,53(12),7429-7445.
MLA Slater, Louise J.,et al."A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed".CLIMATE DYNAMICS 53.12(2019):7429-7445.
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