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Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment, UK

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Assessment of rainfall spatial variability and its influence on runoff modelling : A case study in the Brue catchment, UK. / Zhang, Jun; Han, Dawei.

In: Hydrological Processes, Vol. 31, No. 16, 30.07.2017, p. 2972-2981.

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@article{9a85572ff5694e05a2adc5545427d22a,
title = "Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment, UK",
abstract = "This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficients of variance (CV) and Moran’s I is introduced to describe effective rainfall spatial variability. CV 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 (VIC) 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 (HRUs) 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 implies the feasibility of seasonal models.",
keywords = "CV, Moran's I, rainfall spatial variability, runoff modelling",
author = "Jun Zhang and Dawei Han",
year = "2017",
month = "7",
day = "30",
doi = "10.1002/hyp.11250",
language = "English",
volume = "31",
pages = "2972--2981",
journal = "Hydrological Processes",
issn = "0885-6087",
publisher = "Wiley",
number = "16",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Assessment of rainfall spatial variability and its influence on runoff modelling

T2 - A case study in the Brue catchment, UK

AU - Zhang, Jun

AU - Han, Dawei

PY - 2017/7/30

Y1 - 2017/7/30

N2 - This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficients of variance (CV) and Moran’s I is introduced to describe effective rainfall spatial variability. CV 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 (VIC) 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 (HRUs) 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 implies the feasibility of seasonal models.

AB - This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficients of variance (CV) and Moran’s I is introduced to describe effective rainfall spatial variability. CV 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 (VIC) 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 (HRUs) 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 implies the feasibility of seasonal models.

KW - CV

KW - Moran's I

KW - rainfall spatial variability

KW - runoff modelling

UR - http://www.scopus.com/inward/record.url?scp=85021427828&partnerID=8YFLogxK

U2 - 10.1002/hyp.11250

DO - 10.1002/hyp.11250

M3 - Article

VL - 31

SP - 2972

EP - 2981

JO - Hydrological Processes

JF - Hydrological Processes

SN - 0885-6087

IS - 16

ER -