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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00020711:_____/23:10155050

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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'.

Název v anglickém jazyce

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

Popis výsledku anglicky

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'.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

<a href="/cs/project/SS02030027" target="_blank" >SS02030027: Vodní systémy a vodní hospodářství v ČR v podmínkách změny klimatu</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Hydrology

ISSN

0022-1694

e-ISSN

0022-1694

Svazek periodika

620

Číslo periodika v rámci svazku

129373

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001029671200001

EID výsledku v databázi Scopus

2-s2.0-85151405499