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DOI | 10.1007/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
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
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 |
推荐引用方式 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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