Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25779

Title: A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed
Authors: Slater, Louise
Villarini, Gabriele
Bradley, Allen
Vecchi, Gabriel A.
Keywords: Seasonal forecasting
Probabilistic forecast
Streamflow forecasts
North-American Multi Model ensemble (NMME)
Issue Date: 2017
Publisher: Springer / © The Author(s)
Citation: SLATER, L. ... et al, 2017. A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed. Climate Dynamics, doi:10.1007/s00382-017-3794-7.
Abstract: 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.
Description: This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This study was supported in part by NOAA’s Climate Program Office’s Modeling, Analysis, Predictions, and Projections Program, Grant #NA15OAR4310073, by the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory (CRREL) under Contract No. W913E5-16-C-0002, and by Grant/Cooperative Agreement Number G11 AP20079 from the United States Geological Survey.
Version: Published
DOI: 10.1007/s00382-017-3794-7
URI: https://dspace.lboro.ac.uk/2134/25779
Publisher Link: https://doi.org/10.1007/s00382-017-3794-7
ISSN: 0930-7575
Appears in Collections:Published Articles (Geography)

Files associated with this item:

File Description SizeFormat
10.1007%2Fs00382-017-3794-7.pdfPublished version5.33 MBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.