Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment, UK. Issue 16 (28th June 2017)

• Record Type: Journal Article
• Authors: Zhang, Jun; Han, Dawei
• Is Part Of: Hydrological processes. Volume 31:Issue 16(2017)
• Page Start: 2972
• Page End: 2981
• Publication Date: 2017-06-28
• Subjects: CV -- Moran's I -- rainfall spatial variability -- runoff modelling; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.11250
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficient of variance and Moran's I is introduced to describe effective rainfall spatial variability. Coefficient of variance is widely accepted to identify rainfall variability through rainfall intensity, whereas Moran's I reflects rainfall spatial autocorrelation. This new assessment framework combines these two indicators to assess the spatial variability derived from both rainfall intensity and distribution, which are crucial in determining the time and magnitude of runoff generation. Four model structures embedded in the Variable Infiltration Capacity model are adopted for hydrological modelling in the Brue catchment of England. The models are assigned with 1, 3, 8, and 27 hydrological response units, respectively, and diverse rainfall spatial information for 236 events are extracted from 1995. This study investigates the model performance of different partitioning based on rainfall spatial variability through peak volume (Qp ) and time to peak (Tp ), along with the rainfall event process. The results show that models associated with dense spatial partitioning are broadly capable of capturing more spatial information with better performance. It is unnecessary to utilize models with high spatial density for simple rainfall events, though they show distinct advantages on complex events. With additional spatial information, Qp experiences a notable improvement over Tp . Moreover, seasonal patterns signified by the assessment scheme imply the feasibility of seasonal models.