Choosing an Appropriate Hydrological Model for Rainfall-Runoff Extremes in Small Catchments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00446457" target="_blank" >RIV/67985807:_____/15:00446457 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/44555601:13520/15:43886735 RIV/60460709:41330/15:67781

Výsledek na webu

<a href="http://dx.doi.org/10.17221/16/2015-SWR" target="_blank" >http://dx.doi.org/10.17221/16/2015-SWR</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/16/2015-SWR" target="_blank" >10.17221/16/2015-SWR</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Choosing an Appropriate Hydrological Model for Rainfall-Runoff Extremes in Small Catchments

Popis výsledku v původním jazyce

Real and scenario prognosis in engineering hydrology often involves using simulation techniques of mathematical modelling the rainfall-runoff processes in small catchments. These catchments are often up to 50 km2 in area, their character is torrential, and the type of water flow is super-critical. Many of them are ungauged. The damage in the catchments is enormous, and the length of the torrents is about 23% of the total length of small rivers in the Czech Republic. The Smědá experimental mountainous catchment (with the Bílý potok downstream gauge) in the Jizerské hory Mts. was chosen as a model area for simulating extreme rainfall-runoff processes using two different models. For the purposes of evaluating and simulating significant rainfall-runoff episodes, we chose the KINFIL physically-based 2D hydrological model, and ANN, an artificial neural network mathematical ?learning model. A neural network is a model of the non-linear functional dependence between inputs and outputs with fre

Název v anglickém jazyce

Choosing an Appropriate Hydrological Model for Rainfall-Runoff Extremes in Small Catchments

Popis výsledku anglicky

Real and scenario prognosis in engineering hydrology often involves using simulation techniques of mathematical modelling the rainfall-runoff processes in small catchments. These catchments are often up to 50 km2 in area, their character is torrential, and the type of water flow is super-critical. Many of them are ungauged. The damage in the catchments is enormous, and the length of the torrents is about 23% of the total length of small rivers in the Czech Republic. The Smědá experimental mountainous catchment (with the Bílý potok downstream gauge) in the Jizerské hory Mts. was chosen as a model area for simulating extreme rainfall-runoff processes using two different models. For the purposes of evaluating and simulating significant rainfall-runoff episodes, we chose the KINFIL physically-based 2D hydrological model, and ANN, an artificial neural network mathematical ?learning model. A neural network is a model of the non-linear functional dependence between inputs and outputs with fre

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/TA02020402" target="_blank" >TA02020402: OPTIMALIZACE VODNÍHO REŽIMU KRAJINY KE SNIŽOVÁNÍ DOPADŮ HYDROLOGICKÝCH EXTRÉMŮ</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Soil & Water Research

ISSN

1801-5395

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

137-146

Kód UT WoS článku

000359331100001

EID výsledku v databázi Scopus

2-s2.0-84937836541