Streamflow simulation in poorly gauged basins with regionalised assimilation using Kalman filter

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97279" target="_blank" >RIV/60460709:41330/23:97279 - isvavai.cz</a>

Alternative codes found

RIV/00020711:_____/23:10155050

Result on the web

<a href="http://dx.doi.org/10.1016/j.jhydrol.2023.129373" target="_blank" >http://dx.doi.org/10.1016/j.jhydrol.2023.129373</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jhydrol.2023.129373" target="_blank" >10.1016/j.jhydrol.2023.129373</a>

Result language

angličtina

Original language name

Streamflow simulation in poorly gauged basins with regionalised assimilation using Kalman filter

Original language description

The streamflow estimation in ungauged or poorly gauged basins is a fundamental and challenging problem in hydrology, which has often been solved by transferring hydrological information from gauged basins (i.e. by regionalisation). Most studies on streamflow regionalisation focus on identifying the best methods to transfer the hydrologic model parameters and uses primarily physiographic attributes or climate information for these purposes. In the present study, the sequential data assimilation method - the Kalman filter - has been used to determine streamflow in a poorly gauged (unknown) basin to combine, in an optimal way, observations of neighbouring basins and model simulation of a given basin. The methodology is based on the concept of concatenated upstream catchments, where the aggregation of unobserved states can be estimated. The streamflow estimate is further divided between the unknown sub-catchments using linear regression on the catchments' hydrological characteristics, which are subsequently used to approximate error statistics and operators in the Kalman filter application. The results were evaluated on 165 catchments in the Czech Republic using RMSE, MASE and MAE criteria and indicate that in 87.3% of the cases, the proposed methodology improved the accuracy of streamflow estimations by an average of 40% (in combined evaluated measurements) compared to the original simulations within the system for drought monitoring and forecasting in the Czech Republic 'HAMR'.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

<a href="/en/project/SS02030027" target="_blank" >SS02030027: Water systems and water management in the Czech Republic in conditions of climate change</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Hydrology

ISSN

0022-1694

e-ISSN

0022-1694

Volume of the periodical

620

Issue of the periodical within the volume

129373

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

1-13

UT code for WoS article

001029671200001

EID of the result in the Scopus database

2-s2.0-85151405499