A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

HYDROLOGY AND EARTH SYSTEM SCIENCES(2018)

引用 9|浏览3
暂无评分
摘要
Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macroscale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980-2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to approximate to 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km(2)).
更多
查看译文
关键词
data-assimilation data-assimilation,rainfall–runoff,simulations,macro-scale
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要