A Bayesian method for missing rainfall estimation using a conceptual rainfall-runoff model

被引：8

作者：

Sun, Siao ^{[1
]}

Leonhardt, Guenther ^{[2
,3
]}

Sandoval, Santiago ^{[4
]}

Bertrand-Krajewski, Jean-Luc ^{[4
]}

Rauch, Wolfgang ^{[2
]}

机构：

[1] Chinese Acad Sci, Inst Geog Sci & Nat Resource Res, Key Lab Reg Sustainable Dev Modelling, Beijing, Peoples R China

[2] Univ Innsbruck, Unit Environm Engn, Innsbruck, Austria

[3] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, Lulea, Sweden

[4] Univ Lyon, INSA Lyon, DEEP, Villeurbanne, France

来源：

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES | 2017年 / 62卷 / 15期

关键词：

Bayesian method; conceptual rainfall-runoff model; missing rainfall data; rainfall estimates; uncertainty; UNCERTAINTY;

D O I：

10.1080/02626667.2017.1390317

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

The estimation of missing rainfall data is an important problem for data analysis and modelling studies in hydrology. This paper develops a Bayesian method to address missing rainfall estimation from runoff measurements based on a pre-calibrated conceptual rainfall-runoff model. The Bayesian method assigns posterior probability of rainfall estimates proportional to the likelihood function of measured runoff flows and prior rainfall information, which is presented by uniform distributions in the absence of rainfall data. The likelihood function of measured runoff can be determined via the test of different residual error models in the calibration phase. The application of this method to a French urban catchment indicates that the proposed Bayesian method is able to assess missing rainfall and its uncertainty based only on runoff measurements, which provides an alternative to the reverse model for missing rainfall estimates.

引用

页码：2456 / 2468

页数：13

共 50 条

[31] Automatic calibration of Conceptual rainfall-runoff models
Zhang, Chao
Sun, Ying-ying
PROGRESS IN INDUSTRIAL AND CIVIL ENGINEERING II, PTS 1-4, 2013, 405-408 : 2185 - +
[32] RUNOFF for Windows: a rainfall-runoff model for conceptual analysis and synthesis of hydrographs.
van de Griend, AA
WATER INTERNATIONAL, 1999, 24 (04) : 402 - 404
[33] Bayesian neural network for rainfall-runoff modeling
Khan, Mohammad Sajjad
Coulibaly, Paulin
WATER RESOURCES RESEARCH, 2006, 42 (07)
[34] Rainfall-runoff estimation and comparative analysis using SCS method based on GIS
Alghariani, Mubaraka Saad
DESALINATION AND WATER TREATMENT, 2020, 176 : 148 - 154
[35] THE IMPACT OF THE VARIABILITY OF PRECIPITATION AND TEMPERATURES ON THE EFFICIENCY OF A CONCEPTUAL RAINFALL-RUNOFF MODEL
Sleziak, P.
Szolgay, J.
Hlavcova, K.
Parajka, J.
SLOVAK JOURNAL OF CIVIL ENGINEERING, 2016, 24 (04) : 1 - 7
[36] Evaluation of global optimization methods for conceptual rainfall-runoff model calibration
Cooper, VA
Nguyen, VTV
Nicell, JA
WATER SCIENCE AND TECHNOLOGY, 1997, 36 (05) : 53 - 60
[37] Analysis and development of conceptual rainfall-runoff model structures for regionalisation purposes
Hyosang Lee
Young-Il Moon
KSCE Journal of Civil Engineering, 2007, 11 (1) : 57 - 64
[38] Parameter Optimization of a Conceptual Rainfall-Runoff Model in the Coastal Urban Region
Sunwoo, Wooyeon
Lee, Gyumin
Nguyen, Hoang Hai
Shin, Hyungjin
Jun, Kyung Soo
JOURNAL OF COASTAL RESEARCH, 2021, : 261 - 265
[39] Using a conceptual rainfall-runoff model on different scales by comparing a headwater with larger basins
Uhlenbrook, S
Holocher, J
Leibundgut, C
Seibert, J
HYDROLOGY, WATER RESOURCES AND ECOLOGY IN HEADWATERS, 1998, (248): : 297 - 305
[40] Gradient-Based Inverse Estimation for a Rainfall-Runoff Model
Krapu, Christopher
Borsuk, Mark
Kumar, Mukesh
WATER RESOURCES RESEARCH, 2019, 55 (08) : 6625 - 6639

← 1 2 3 4 5 →