Deriving the storage function model parameters by using runoff data only

Joko Sujono, Shiomi Shikasho, Kazuaki Hiramatsu

Research output: Contribution to journalArticle

Abstract

Rainfall-runoff models require rainfall and runoff data for determining the model parameters. Problem usually emerges in finding the optimal model parameters due to lack of rainfall data in terms of quantity and quality. In a lumped model such as the storage function model, good quality and quantity of input (rainfall) data that represent the catchment behavior is needed in order to get the optimum model parameters. However, it is difficult to get the rainfall data that fulfill the requirement as results of high spatial variability of rainfall data and lack of automatic rainfall recorder that are commonly found in tropical regions. To overcome the above problem, the filter separation autoregressive model might be used to estimate the rainfall time series based on runoff data only. The resulted rainfall together with the runoff data are then used to find the storage function model parameters. The results show that the inversely estimated rainfall was useful for estimating the rainfall-runoff model parameters in topical regions.

Original languageEnglish
Pages (from-to)129-138
Number of pages10
JournalJournal of the Faculty of Agriculture, Kyushu University
Volume47
Issue number1
Publication statusPublished - Oct 1 2002

Fingerprint

runoff
rain
meteorological data
hydrologic models
time series analysis
tropics

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Agronomy and Crop Science

Cite this

Deriving the storage function model parameters by using runoff data only. / Sujono, Joko; Shikasho, Shiomi; Hiramatsu, Kazuaki.

In: Journal of the Faculty of Agriculture, Kyushu University, Vol. 47, No. 1, 01.10.2002, p. 129-138.

Research output: Contribution to journalArticle

@article{a5dfe6cd7b504ef4965ceb31b27920f8,
title = "Deriving the storage function model parameters by using runoff data only",
abstract = "Rainfall-runoff models require rainfall and runoff data for determining the model parameters. Problem usually emerges in finding the optimal model parameters due to lack of rainfall data in terms of quantity and quality. In a lumped model such as the storage function model, good quality and quantity of input (rainfall) data that represent the catchment behavior is needed in order to get the optimum model parameters. However, it is difficult to get the rainfall data that fulfill the requirement as results of high spatial variability of rainfall data and lack of automatic rainfall recorder that are commonly found in tropical regions. To overcome the above problem, the filter separation autoregressive model might be used to estimate the rainfall time series based on runoff data only. The resulted rainfall together with the runoff data are then used to find the storage function model parameters. The results show that the inversely estimated rainfall was useful for estimating the rainfall-runoff model parameters in topical regions.",
author = "Joko Sujono and Shiomi Shikasho and Kazuaki Hiramatsu",
year = "2002",
month = "10",
day = "1",
language = "English",
volume = "47",
pages = "129--138",
journal = "Journal of the Faculty of Agriculture, Kyushu University",
issn = "0023-6152",
publisher = "Faculty of Agriculture, Kyushu University",
number = "1",

}

TY - JOUR

T1 - Deriving the storage function model parameters by using runoff data only

AU - Sujono, Joko

AU - Shikasho, Shiomi

AU - Hiramatsu, Kazuaki

PY - 2002/10/1

Y1 - 2002/10/1

N2 - Rainfall-runoff models require rainfall and runoff data for determining the model parameters. Problem usually emerges in finding the optimal model parameters due to lack of rainfall data in terms of quantity and quality. In a lumped model such as the storage function model, good quality and quantity of input (rainfall) data that represent the catchment behavior is needed in order to get the optimum model parameters. However, it is difficult to get the rainfall data that fulfill the requirement as results of high spatial variability of rainfall data and lack of automatic rainfall recorder that are commonly found in tropical regions. To overcome the above problem, the filter separation autoregressive model might be used to estimate the rainfall time series based on runoff data only. The resulted rainfall together with the runoff data are then used to find the storage function model parameters. The results show that the inversely estimated rainfall was useful for estimating the rainfall-runoff model parameters in topical regions.

AB - Rainfall-runoff models require rainfall and runoff data for determining the model parameters. Problem usually emerges in finding the optimal model parameters due to lack of rainfall data in terms of quantity and quality. In a lumped model such as the storage function model, good quality and quantity of input (rainfall) data that represent the catchment behavior is needed in order to get the optimum model parameters. However, it is difficult to get the rainfall data that fulfill the requirement as results of high spatial variability of rainfall data and lack of automatic rainfall recorder that are commonly found in tropical regions. To overcome the above problem, the filter separation autoregressive model might be used to estimate the rainfall time series based on runoff data only. The resulted rainfall together with the runoff data are then used to find the storage function model parameters. The results show that the inversely estimated rainfall was useful for estimating the rainfall-runoff model parameters in topical regions.

UR - http://www.scopus.com/inward/record.url?scp=2442565826&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2442565826&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:2442565826

VL - 47

SP - 129

EP - 138

JO - Journal of the Faculty of Agriculture, Kyushu University

JF - Journal of the Faculty of Agriculture, Kyushu University

SN - 0023-6152

IS - 1

ER -